U.S. patent number 11,221,340 [Application Number 15/993,132] was granted by the patent office on 2022-01-11 for lung cancer biomarkers and uses thereof.
This patent grant is currently assigned to SomaLogic, Inc.. The grantee listed for this patent is SomaLogic, Inc.. Invention is credited to Deborah Ayers, Larry Gold, Nebojsa Janjic, Thale Jarvis, Michael Riel-Mehan, Sheri K. Wilcox.
United States Patent |
11,221,340 |
Wilcox , et al. |
January 11, 2022 |
Lung cancer biomarkers and uses thereof
Abstract
The present application includes biomarkers, methods, devices,
reagents, systems, and kits for the detection and diagnosis of lung
cancer. In one aspect, the application provides biomarkers that can
be used alone or in various combinations to diagnose lung cancer or
permit the differential diagnosis of pulmonary nodules as benign or
malignant. In another aspect, methods are provided for diagnosing
lung cancer in an individual, where the methods include detecting,
in a biological sample from an individual, at least one biomarker
value corresponding to at least one biomarker selected from the
group of biomarkers provided in Table 18, Table 20, or Table 21,
wherein the individual is classified as having lung cancer, or the
likelihood of the individual having lung cancer is determined,
based on the at least one biomarker value.
Inventors: |
Wilcox; Sheri K. (Longmont,
CO), Ayers; Deborah (Broomfield, CO), Janjic; Nebojsa
(Boulder, CO), Gold; Larry (Boulder, CO), Riel-Mehan;
Michael (San Marcos, CA), Jarvis; Thale (Boulder,
CO) |
Applicant: |
Name |
City |
State |
Country |
Type |
SomaLogic, Inc. |
Boulder |
CO |
US |
|
|
Assignee: |
SomaLogic, Inc. (Boulder,
CO)
|
Family
ID: |
1000006042923 |
Appl.
No.: |
15/993,132 |
Filed: |
May 30, 2018 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20180275143 A1 |
Sep 27, 2018 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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13808751 |
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PCT/US2011/043595 |
Jul 11, 2011 |
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61444947 |
Feb 21, 2011 |
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61363122 |
Jul 9, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B
25/10 (20190201); G01N 33/57423 (20130101); G16B
99/00 (20190201); G01N 33/6893 (20130101); C12Q
1/6886 (20130101); G16B 20/00 (20190201); G16B
40/30 (20190201); G16B 20/20 (20190201); C12Q
2600/106 (20130101); G16B 25/00 (20190201); G16B
40/00 (20190201); C12Q 2600/16 (20130101); C12Q
2600/156 (20130101); C12Q 2600/112 (20130101) |
Current International
Class: |
G01N
33/68 (20060101); G16B 99/00 (20190101); C12Q
1/6886 (20180101); G16B 20/00 (20190101); G01N
33/574 (20060101); G16B 40/30 (20190101); G16B
25/10 (20190101); G16B 20/20 (20190101); G16B
40/00 (20190101); G16B 25/00 (20190101) |
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|
Primary Examiner: Flinders; Jeremy C
Attorney, Agent or Firm: Leydig, Voit & Mayer, Ltd.
Parent Case Text
RELATED APPLICATIONS
This application is a continuation application of U.S. application
Ser. No. 13/808,751, filed Jan. 7, 2013, which is a 35 U.S.C.
.sctn. 371 national phase application of PCT/US2011/043595, filed
Jul. 11, 2011, which claims the benefit of U.S. Provisional
Application Ser. No. 61/363,122, filed Jul. 9, 2010 and U.S.
Provisional Application Ser. No. 61/444,947, filed Feb. 21, 2011,
each of which is entitled "Lung Cancer Biomarkers and Uses
Thereof". Each of these applications are incorporated herein by
reference in its entirety for all purposes.
Claims
What is claimed is:
1. A method comprising the following steps: a) contacting a
biological sample from a human with a set of N capture reagents,
wherein N is any integer from 5 to 20, wherein the biological
sample is selected from the group consisting of whole blood,
plasma, and serum, further wherein the capture reagents are
aptamers comprising a 5-position pyrimidine modification, further
wherein each capture reagent specifically binds to a different
protein of the set of proteins comprising at least ERBB1, SCFsR,
WIMP-12, C9 and MMP-7; b) measuring the level of each protein of
the set of proteins based on measurement of the capture reagents;
c) applying a trained nave Bayes classifier, trained with data from
a lung cancer negative group and a lung cancer positive group, to
the measured level of each of the different proteins in order to
calculate an individual log-likelihood ratio for each of the
different proteins; d) determining a risk level of the human for
having lung cancer based on the sum of each log-likelihood ratio
for each of the different proteins plus a term to account for the
prevalence of lung cancer in a population comprising the human; and
e) administering a treatment selected from the group consisting of
a cancer vaccine, radiation, drug therapy, surgery and a
combination thereof to the subject.
2. The method of claim 1, wherein measurement of the protein levels
comprises performing an in vitro assay.
3. The method of claim 1, wherein the biological sample is
serum.
4. The method of claim 1, wherein the human is a smoker.
5. The method of claim 1, wherein the human has a pulmonary nodule.
Description
FIELD OF THE INVENTION
The present application relates generally to the detection of
biomarkers and the diagnosis of cancer in an individual and, more
specifically, to one or more biomarkers, methods, devices,
reagents, systems, and kits for diagnosing cancer, more
particularly lung cancer, in an individual.
BACKGROUND
The following description provides a summary of information
relevant to the present application and is not an admission that
any of the information provided or publications referenced herein
is prior art to the present application.
Lung cancer remains the most common cause of cancer-related
mortality. This is true for both men and women. In 2005 in the
United States lung cancer accounted for more deaths than breast
cancer, prostate cancer, and colon cancer combined. In that year,
107,416 men and 89,271 women were diagnosed with lung cancer, and
90,139 men and 69,078 women died from lung cancer. Among men in the
United States, lung cancer is the second most common cancer among
white, black, Asian/Pacific Islander, American Indian/Alaska
Native, and Hispanic men. Among women in the United States, lung
cancer is the second most common cancer among white, black, and
American Indian/Alaska Native women, and the third most common
cancer among Asian/Pacific Islander and Hispanic women. For those
who do not quit smoking, the probability of death from lung cancer
is 15% and remains above 5% even for those who quit at age 50-59.
The annual healthcare cost of lung cancer in the U.S. alone is S95
billion.
Ninety-one percent of lung cancer caused by smoking is non-small
cell lung cancer (NSCLC), which represents approximately 87% of all
lung cancers. The remaining 13% of all lung cancers are small cell
lung cancers, although mixed-cell lung cancers do occur. Because
small cell lung cancer is rare and rapidly fatal, the opportunity
for early detection is small.
There are three main types of NSCLC: squamous cell carcinoma, large
cell carcinoma, and adenocarcinoma. Adenocarcinoma is the most
common form of lung cancer (30%-40% and reported to be as high as
50%) and is the lung cancer most frequently found in both smokers
and non-smokers. Squamous cell carcinoma accounts for 25-30% of all
lung cancers and is generally found in a proximal bronchus. Early
stage NSCLC tends to be localized, and if detected early it can
often be treated by surgery with a favorable outcome and improved
survival. Other treatment options include radiation treatment, drug
therapy, and a combination of these methods.
NSCLC is staged by the size of the tumor and its presence in other
tissues including lymph nodes. In the occult stage, cancer cells
are found in sputum samples or lavage samples and no tumor is
detectable in the lungs. In stage 0, only the innermost lining of
the lungs exhibit cancer cells and the tumor has not grown through
the lining. In stage IA, the cancer is considered invasive and has
grown deep into the lung tissue but the tumor is less than 3 cm
across. In this stage, the tumor is not found in the bronchus or
lymph nodes. In stage IB, the tumor is either larger than 3 cm
across or has grown into the bronchus or pleura, but has not grown
into the lymph nodes. In stage IIA, the tumor is more than 3 cm
across and has grown into the lymph nodes. In stage IIB, the tumor
has either been found in the lymph nodes and is greater than 3 cm
across or grown into the bronchus or pleura; or the cancer is not
in the lymph nodes but is found in the chest wall, diaphragm,
pleura, bronchus, or tissue that surrounds the heart. In stage
IIIA, cancer cells are found in the lymph nodes near the lung and
bronchi and in those between the lungs but on the side of the chest
where the tumor is located. Stage IIIB, cancer cells are located on
the opposite side of the chest from the tumor and in the neck.
Other organs near the lungs may also have cancer cells and multiple
tumors may be found in one lobe of the lungs. In stage IV, tumors
are found in more than one lobe of the same lung or both lungs and
cancer cells are found in other parts of the body.
Current methods of diagnosis for lung cancer include testing sputum
for cancerous cells, chest x-ray, fiber optic evaluation of
airways, and low dose spiral computed tomography (CT). Sputum
cytology has a very low sensitivity. Chest X-ray is also relatively
insensitive, requiring lesions to be greater than 1 cm in size to
be visible. Bronchoscopy requires that the tumor is visible inside
airways accessible to the bronchoscope. The most widely recognized
diagnostic method is CT, but in common with X-ray, the use of CT
involves ionizing radiation, which itself can cause cancer. CT also
has significant limitations: the scans require a high level of
technical skill to interpret and many of the observed abnormalities
are not in fact lung cancer and substantial healthcare costs are
incurred in following up CT findings. The most common incidental
finding is a benign lung nodule.
Lung nodules are relatively round lesions, or areas of abnormal
tissue, located within the lung and may vary in size. Lung nodules
may be benign or cancerous, but most are benign. If a nodule is
below 4 mm the prevalence is only 1.5%, if 4-8 mm the prevalence is
approximately 6%, and if above 20 mm the incidence is approximately
20%. For small and medium-sized nodules, the patient is advised to
undergo a repeat scan within three months to a year. For many large
nodules, the patient receives a biopsy (which is invasive and may
lead to complications) even though most of these are benign.
Therefore, diagnostic methods that can replace or complement CT are
needed to reduce the number of surgical procedures conducted and
minimize the risk of surgical complications. In addition, even when
lung nodules are absent or unknown, methods are needed to detect
lung cancer at its early stages to improve patient outcomes. Only
16% of lung cancer cases are diagnosed as localized, early stage
cancer, where the 5-year survival rate is 46%, compared to 84% of
those diagnosed at late stage, where the 5-year survival rate is
only 13%. This demonstrates that relying on symptoms for diagnosis
is not useful because many of them are common to other lung
disease. These symptoms include a persistent cough, bloody sputum,
chest pain, and recurring bronchitis or pneumonia.
Where methods of early diagnosis of cancer exist, the benefits are
generally accepted by the medical community. Cancers that have
widely utilized screening protocols have the highest 5-year
survival rates, such as breast cancer (88%) and colon cancer (65%)
versus 16% for lung cancer. However, 88% of lung cancer patients
survive ten years or longer if the cancer is diagnosed at Stage 1
through screening. This demonstrates the clear need for diagnostic
methods that can reliably detect early-stage NSCLC.
Progression from healthy state to disease is accompanied by changes
in protein expression in affected tissues. Comparative
interrogation of the human proteome in healthy and diseased tissues
can offer insights into the biology of disease and lead to
discovery of biomarkers for diagnostics, new targets for
therapeutic intervention, and identification of patients most
likely to benefit from targeted treatment. Biomarker selection for
a specific disease state involves first the identification of
markers that have a measurable and statistically significant
difference in a disease population compared to a control population
for a specific medical application. Biomarkers can include secreted
or shed molecules that parallel disease development or progression
and readily diffuse into the blood stream from lung tissue or from
distal tissues in response to a lesion. The biomarker or set of
biomarkers identified are generally clinically validated or shown
to be a reliable indicator for the original intended use for which
it was selected. Biomarkers can include small molecules, peptides,
proteins, and nucleic acids. Some of the key issues that affect the
identification of biomarkers include over-fitting of the available
data and bias in the data.
A variety of methods have been utilized in an attempt to identify
biomarkers and diagnose disease. For protein-based markers, these
include two-dimensional electrophoresis, mass spectrometry, and
immunoassay methods. For nucleic acid markers, these include mRNA
expression profiles, microRNA profiles, FISH, serial analysis of
gene expression (SAGE), and large scale gene expression arrays.
The utility of two-dimensional electrophoresis is limited by low
detection sensitivity; issues with protein solubility, charge, and
hydrophobicity; gel reproducibility; and the possibility of a
single spot representing multiple proteins. For mass spectrometry,
depending on the format used, limitations revolve around the sample
processing and separation, sensitivity to low abundance proteins,
signal to noise considerations, and inability to immediately
identify the detected protein. Limitations in immunoassay
approaches to biomarker discovery are centered on the inability of
antibody-based multiplex assays to measure a large number of
analytes. One might simply print an array of high-quality
antibodies and, without sandwiches, measure the analytes bound to
those antibodies. (This would be the formal equivalent of using a
whole genome of nucleic acid sequences to measure by hybridization
all DNA or RNA sequences in an organism or a cell. The
hybridization experiment works because hybridization can be a
stringent test for identity. Even very good antibodies are not
stringent enough in selecting their binding partners to work in the
context of blood or even cell extracts because the protein ensemble
in those matrices have extremely different abundances.) Thus, one
must use a different approach with immunoassay-based approaches to
biomarker discovery--one would need to use multiplexed ELISA assays
(that is, sandwiches) to get sufficient stringency to measure many
analytes simultaneously to decide which analytes are indeed
biomarkers. Sandwich immunoassays do not scale to high content, and
thus biomarker discovery using stringent sandwich immunoassays is
not possible using standard array formats. Lastly, antibody
reagents are subject to substantial lot variability and reagent
instability. The instant platform for protein biomarker discovery
overcomes this problem.
Many of these methods rely on or require some type of sample
fractionation prior to the analysis. Thus the sample preparation
required to run a sufficiently powered study designed to
identify/discover statistically relevant biomarkers in a series of
well-defined sample populations is extremely difficult, costly, and
time consuming. During fractionation, a wide range of variability
can be introduced into the various samples. For example, a
potential marker could be unstable to the process, the
concentration of the marker could be changed, inappropriate
aggregation or disaggregation could occur, and inadvertent sample
contamination could occur and thus obscure the subtle changes
anticipated in early disease.
It is widely accepted that biomarker discovery and detection
methods using these technologies have serious limitations for the
identification of diagnostic biomarkers. These limitations include
an inability to detect low-abundance biomarkers, an inability to
consistently cover the entire dynamic range of the proteome,
irreproducibility in sample processing and fractionation, and
overall irreproducibility and lack of robustness of the method.
Further, these studies have introduced biases into the data and not
adequately addressed the complexity of the sample populations,
including appropriate controls, in terms of the distribution and
randomization required to identify and validate biomarkers within a
target disease population.
Although efforts aimed at the discovery of new and effective
biomarkers have gone on for several decades, the efforts have been
largely unsuccessful. Biomarkers for various diseases typically
have been identified in academic laboratories, usually through an
accidental discovery while doing basic research on some disease
process. Based on the discovery and with small amounts of clinical
data, papers were published that suggested the identification of a
new biomarker. Most of these proposed biomarkers, however, have not
been confirmed as real or useful biomarkers primarily because the
small number of clinical samples tested provide only weak
statistical proof that an effective biomarker has in fact been
found. That is, the initial identification was not rigorous with
respect to the basic elements of statistics. In each of the years
1994 through 2003, a search of the scientific literature shows that
thousands of references directed to biomarkers were published.
During that same time frame, however, the FDA approved for
diagnostic use, at most, three new protein biomarkers a year, and
in several years no new protein biomarkers were approved.
Based on the history of failed biomarker discovery efforts,
mathematical theories have been proposed that further promote the
general understanding that biomarkers for disease are rare and
difficult to find. Biomarker research based on 2D gels or mass
spectrometry supports these notions. Very few useful biomarkers
have been identified through these approaches. However, it is
usually overlooked that 2D gel and mass spectrometry measure
proteins that are present in blood at approximately 1 nM
concentrations and higher, and that this ensemble of proteins may
well be the least likely to change with disease. Other than the
instant biomarker discovery platform, proteomic biomarker discovery
platforms that are able to accurately measure protein expression
levels at much lower concentrations do not exist.
Much is known about biochemical pathways for complex human biology.
Many biochemical pathways culminate in or are started by secreted
proteins that work locally within the pathology, for example growth
factors are secreted to stimulate the replication of other cells in
the pathology, and other factors are secreted to ward off the
immune system, and so on. While many of these secreted proteins
work in a paracrine fashion, some operate distally in the body. One
skilled in the art with a basic understanding of biochemical
pathways would understand that many pathology-specific proteins
ought to exist in blood at concentrations below (even far below)
the detection limits of 2D gels and mass spectrometry. What must
precede the identification of this relatively abundant number of
disease biomarkers is a proteomic platform that can analyze
proteins at concentrations below those detectable by 2D gels or
mass spectrometry.
Accordingly, a need exists for biomarkers, methods, devices,
reagents, systems, and kits that enable (a) the differentiation of
benign pulmonary nodules from malignant pulmonary nodules; (b) the
detection of lung cancer biomarkers; and (c) the diagnosis of lung
cancer.
To fulfill this need, a novel aptamer-based proteomic technology
for biomarker discovery, which is capable of simultaneously
measuring thousands of proteins from small sample volumes of plasma
or serum has been developed (see e.g., U.S. Pub. No. 2010/0070191;
U.S. Pub. No. 2010/0086948, Ostroff et al., "Unlocking biomarker
discovery: Large scale application of aptamer proteomic technology
for early detection of lung cancer," Nature Precedings, (2010);
Gold et al., "Aptamer-based multiplexed proteomic technology for
biomarker discovery," Nature Precedings, (2010)). This technology,
referred to as SOMAscan, is enabled by a new generation of slow
off-rate aptamers (SOMAmers) that contain chemically modified
nucleotides, which greatly expand the physicochemical diversity of
the large randomized nucleic acid libraries from which the aptamers
are selected (see U.S. Pat. No. 7,947,447). Such modifications,
which are compatible with SELEX, introduce functional groups into
aptamers that are often found in protein-protein interaction,
antibody-antigen interactions and interactions between
small-molecule drugs with their protein targets. Overall, the use
of these modifications expands the range of possible aptamer
targets, improves their binding properties and facilitates
selection of aptamers with slow dissociation rates.
Specifically, proteins in complex matrices such as plasma are
measured with a process that transforms a signature of protein
concentrations into a corresponding signature of DNA aptamer
concentrations, which is then quantified using a DNA microarray
platform (Gold et al., "Aptamer-based multiplexed proteomic
technology for biomarker discovery," Nature Precedings, (2010)).
The assay leverages equilibrium binding and kinetic challenge. Both
are carried out in solution, not on a surface, to take advantage of
more favorable kinetics of binding and dissociation. In essence,
the assay takes advantage of the dual nature of aptamers as both
folded binding entities with defined shapes and unique sequences
recognizable by specific hybridization probes.
The assay is capable of simultaneously measuring large numbers of
proteins ranging from low to high abundance in serum. For example,
samples from 1,326 subjects from four independent studies of
non-small cell lung cancer (NSCLC) have been analyzed in long-term
tobacco-exposed populations. More than 800 proteins in 15 .mu.L of
serum were measured and a 12-protein panel was developed that
distinguishes NSCLC from controls with 91% sensitivity and 84%
specificity in a training set and 89% sensitivity and 83%
specificity in a blinded, independent verification set.
Importantly, performance was similar for early and late stage NSCLC
(Ostroff et al., Unlocking biomarker discovery: Large scale
application of aptamer proteomic technology for early detection of
lung cancer," Nature Precedings, (2010)).
To date, several clinical biomarker studies of human diseases,
including lung cancer (U.S. Pub. No. 2010/0070191), ovarian cancer
(U.S. Pub. No. 2010/0086948), and chronic kidney disease have been
conducted using this method. These studies have identified novel
potential disease biomarkers to each of these diseases as well as
to cancer in general.
SUMMARY
The present application demonstrates the utility of the newly
discovered microarray platform technology to identify
disease-related biomarkers from tissue. The present application
includes biomarkers, methods, reagents, devices, systems, and kits
for the detection and diagnosis of cancer and more particularly,
lung cancer from tissue. The biomarkers of the present application
were identified using a multiplex aptamer-based assay which is
described in detail in Example 6. By using the aptamer-based
biomarker identification method described herein, this application
describes a surprisingly large number of lung cancer biomarkers
from tissue that are useful for the detection and diagnosis of lung
cancer. In identifying these biomarkers, over 800 proteins from a
number of individual samples were measured, some of which were at
concentrations in the low femtomolar range. This is about four
orders of magnitude lower than biomarker discovery experiments done
with 2D gels and/or mass spectrometry.
While certain of the described lung cancer biomarkers are useful
alone for detecting and diagnosing lung cancer, methods are
described herein for the grouping of multiple subsets of the lung
cancer biomarkers that are useful as a panel of biomarkers. Once an
individual biomarker or subset of biomarkers has been identified,
the detection or diagnosis of lung cancer in an individual can be
accomplished using any assay platform or format that is capable of
measuring differences in the levels of the selected biomarker or
biomarkers in a biological sample.
However, it was only by using the aptamer-based biomarker
identification method described herein, wherein over 800 separate
potential biomarker values were individually screened from a large
number of individuals having previously been diagnosed either as
having or not having lung cancer that it was possible to identify
the lung cancer biomarkers disclosed herein. This discovery
approach is in stark contrast to biomarker discovery from
conditioned media or lysed cells as it queries a more
patient-relevant system that requires no translation to human
pathology.
Thus, in one aspect of the instant application, one or more
biomarkers are provided for use either alone or in various
combinations to diagnose lung cancer, particularly non-small cell
lung cancer (NSCLC) or permit the differential diagnosis of
pulmonary nodules as benign or malignant. Exemplary embodiments
include the biomarkers provided in Table 18, which as noted above,
were identified using a multiplex aptamer-based assay, as described
generally in Example 1 and more specifically in Example 6. The
markers provided in Table 18 are useful in distinguishing benign
nodules from cancerous nodules. The markers provided in Table 18
are also useful in distinguishing asymptomatic smokers from smokers
having lung cancer. In one aspect the biomarker is MMP-7. In
another aspect the biomarker is MMP-12.
While certain of the described lung cancer biomarkers are useful
alone for detecting and diagnosing lung cancer, methods are also
described herein for the grouping of multiple subsets of the lung
cancer biomarkers that are each useful as a panel of two or more
biomarkers. Thus, various embodiments of the instant application
provide combinations comprising N biomarkers, wherein N is at least
two biomarkers. In other embodiments, N is selected to be any
number from 2-36 biomarkers.
In yet other embodiments, N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25, 2-30, 2-36. In other embodiments, N is
selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30,
3-36. In other embodiments, Nis selected to be any number from 4-7,
4-10, 4-15, 4-20, 4-25, 4-30, 4-36. In other embodiments, N is
selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30,
5-36. In other embodiments, N is selected to be any number from
6-10, 6-15, 6-20, 6-25, 6-30, 6-36. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-36.
In other embodiments, N is selected to be any number from 8-10,
8-15, 8-20, 8-25, 8-30, 8-36. In other embodiments, N is selected
to be any number from 9-15, 9-20, 9-25, 9-30, 9-36. In other
embodiments, N is selected to be any number from 10-15, 10-20,
10-25, 10-30, 10-36. It will be appreciated that N can be selected
to encompass similar, but higher order, ranges.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers provided in Table 18, wherein the individual is
classified as having lung cancer based on the at least one
biomarker value.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 18, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 18, wherein the individual is classified as
having lung cancer based on the biomarker values, and wherein
N=2-10.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 18, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values, and
wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers set forth in Table 18, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on the at least
one biomarker value.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 18, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on said
biomarker values, wherein N=2-10.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, at least one
biomarker value corresponding to at least one biomarker selected
from the group of biomarkers set forth in Table 18, wherein the
individual is classified as not having lung cancer based on the at
least one biomarker value.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, biomarker
values that each corresponding to one of at least N biomarkers
selected from the group of biomarkers set forth in Table 18,
wherein the individual is classified as not having lung cancer
based on the biomarker values, and wherein N=2-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 18, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 18, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-15.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 18, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, and wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 18, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 18,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-10.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 18,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-15.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that correspond to one
of at least N biomarkers selected from the group of biomarkers set
forth in Table 18, wherein the individual is classified as having
lung cancer based on a classification score that deviates from a
predetermined threshold, and wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 18, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 18, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer in an individual, the method including detecting, in
a biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 18, wherein said individual is
classified as not having lung cancer based on a classification
score that deviates from a predetermined threshold, and wherein
N=2-10.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises:
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises biomarker values that
each correspond to one of at least N biomarkers, wherein N is as
defined above, selected from the group of biomarkers set forth in
Table 18; performing with the computer a classification of each of
the biomarker values; and indicating a likelihood that the
individual has lung cancer based upon a plurality of
classifications.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises: retrieving on a computer biomarker
information for an individual, wherein the biomarker information
comprises biomarker values that each correspond to one of at least
N biomarkers selected from the group of biomarkers provided in
Table 18; performing with the computer a classification of each of
the biomarker values; and indicating whether the individual has
lung cancer based upon a plurality of classifications.
In another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers, wherein N is as defined above, in the biological sample
selected from the group of biomarkers set forth in Table 18; and
code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker values.
In another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises biomarker values that each correspond to one of at
least N biomarkers in the biological sample selected from the group
of biomarkers provided in Table 18; and code that executes a
classification method that indicates a lung cancer status of the
individual as a function of the biomarker values.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises a biomarker value
corresponding to a biomarker selected from the group of biomarkers
set forth in Table 18; performing with the computer a
classification of the biomarker value; and indicating a likelihood
that the individual has lung cancer based upon the
classification.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises retrieving from a computer biomarker
information for an individual, wherein the biomarker information
comprises a biomarker value corresponding to a biomarker selected
from the group of biomarkers provided in Table 18; performing with
the computer a classification of the biomarker value; and
indicating whether the individual has lung cancer based upon the
classification.
In still another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises a
biomarker value corresponding to a biomarker in the biological
sample selected from the group of biomarkers set forth in Table 18;
and code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker value.
In still another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises a biomarker value corresponding to a biomarker in
the biological sample selected from the group of biomarkers
provided in Table 18; and code that executes a classification
method that indicates a lung cancer status of the individual as a
function of the biomarker value.
In another embodiment of the instant application, exemplary
embodiments include the biomarkers provided in Table 20, which as
noted above, were identified using a multiplex aptamer-based assay,
as described generally in Example 1 and more specifically in
Example 6. The markers provided in Table 20 are useful in
distinguishing benign nodules from cancerous nodules. The markers
provided in Table 20 are also useful in distinguishing asymptomatic
smokers from smokers having lung cancer. With reference to Table
20, N is selected to be any number from 2-25 biomarkers. The
markers provided in Table 20 have been determined to be useful in
both tissue and serum samples.
In yet other embodiments, N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25. In other embodiments, N is selected to be
any number from 3-7, 3-10, 3-15, 3-20, 3-25. In other embodiments,
N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25. In
other embodiments, N is selected to be any number from 5-7, 5-10,
5-15, 5-20, 5-25. In other embodiments, N is selected to be any
number from 6-10, 6-15, 6-20, 6-25. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25. In other
embodiments, N is selected to be any number from 8-10, 8-15, 8-20,
8-25. In other embodiments, N is selected to be any number from
9-15, 9-20, 9-25. In other embodiments, N is selected to be any
number from 10-15, 10-20, 10-25. It will be appreciated that N can
be selected to encompass similar, but higher order, ranges.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers provided in Table 20, wherein the individual is
classified as having lung cancer based on the at least one
biomarker value.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 20, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 20, wherein the individual is classified as
having lung cancer based on the biomarker values, and wherein
N=2-10.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 20, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values, and
wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers set forth in Table 20, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on the at least
one biomarker value.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 20, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on said
biomarker values, wherein N=2-10.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, at least one
biomarker value corresponding to at least one biomarker selected
from the group of biomarkers set forth in Table 20, wherein the
individual is classified as not having lung cancer based on the at
least one biomarker value.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, biomarker
values that each corresponding to one of at least N biomarkers
selected from the group of biomarkers set forth in Table 20,
wherein the individual is classified as not having lung cancer
based on the biomarker values, and wherein N=2-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 20, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 20, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-15.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 20, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, and wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 20, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 20,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-10.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 20,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-15.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that correspond to one
of at least N biomarkers selected from the group of biomarkers set
forth in Table 20, wherein the individual is classified as having
lung cancer based on a classification score that deviates from a
predetermined threshold, and wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 20, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 20, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer in an individual, the method including detecting, in
a biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 20, wherein said individual is
classified as not having lung cancer based on a classification
score that deviates from a predetermined threshold, and wherein
N=2-10.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises:
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises biomarker values that
each correspond to one of at least N biomarkers, wherein N is as
defined above, selected from the group of biomarkers set forth in
Table 20; performing with the computer a classification of each of
the biomarker values; and indicating a likelihood that the
individual has lung cancer based upon a plurality of
classifications.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises: retrieving on a computer biomarker
information for an individual, wherein the biomarker information
comprises biomarker values that each correspond to one of at least
N biomarkers selected from the group of biomarkers provided in
Table 20; performing with the computer a classification of each of
the biomarker values; and indicating whether the individual has
lung cancer based upon a plurality of classifications.
In another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers, wherein N is as defined above, in the biological sample
selected from the group of biomarkers set forth in Table 20; and
code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker values.
In another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises biomarker values that each correspond to one of at
least N biomarkers in the biological sample selected from the group
of biomarkers provided in Table 20; and code that executes a
classification method that indicates a lung cancer status of the
individual as a function of the biomarker values.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises a biomarker value
corresponding to a biomarker selected from the group of biomarkers
set forth in Table 20; performing with the computer a
classification of the biomarker value; and indicating a likelihood
that the individual has lung cancer based upon the
classification.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises retrieving from a computer biomarker
information for an individual, wherein the biomarker information
comprises a biomarker value corresponding to a biomarker selected
from the group of biomarkers provided in Table 20; performing with
the computer a classification of the biomarker value; and
indicating whether the individual has lung cancer based upon the
classification.
In still another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises a
biomarker value corresponding to a biomarker in the biological
sample selected from the group of biomarkers set forth in Table 20;
and code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker value.
In still another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises a biomarker value corresponding to a biomarker in
the biological sample selected from the group of biomarkers
provided in Table 20; and code that executes a classification
method that indicates a lung cancer status of the individual as a
function of the biomarker value.
In another embodiment of the instant application, exemplary
embodiments include the biomarkers provided in Table 21, which were
identified using a multiplex aptamer-based assay, as described
generally in Example 1 and more specifically in Examples 2 and 6.
The markers provided in Table 21 are useful in distinguishing
benign nodules from cancerous nodules. The markers provided in
Table 21 are also useful in distinguishing asymptomatic smokers
from smokers having lung cancer. With reference to Table 21, N is
selected to be any number from 2-86 biomarkers. All of the
biomarkers included in Table 21 are useful in providing the
information being sought in both tissue and serum samples.
In yet other embodiments, N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50, 2-55, 2-60,
2-65, 2-70, 2-75, 2-80, or 2-86. In other embodiments, N is
selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30,
3-35, 3-40, 3-45, 3-50, 3-55, 3-60, 3-65, 3-70, 3-75, 3-80, or
3-86. In other embodiments, N is selected to be any number from
4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50, 4-55,
4-60, 4-65, 4-70, 4-75, 4-80, or 4-86. In other embodiments, N is
selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30,
5-35, 5-40, 5-45, 5-50, 5-55, 5-60, 5-65, 5-70, 5-75, 5-80, or
5-86. In other embodiments, N is selected to be any number from
6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, 6-60,
6-65, 6-70, 6-75, 6-80, or 6-86. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35,
7-40, 7-45, 7-50, 7-55, 7-60, 7-65, 7-70, 7-75, 7-80, or 7-86. In
other embodiments, N is selected to be any number from 8-10, 8-15,
8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, 8-60, 8-65, 8-70,
8-75, 8-80, or 8-86. In other embodiments, N is selected to be any
number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55,
9-60, 9-65, 9-70, 9-75, 9-80, or 9-86. In other embodiments, N is
selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35,
10-40, 10-45, 10-50, 10-55, 10-60, 10-65, 10-70, 10-75, 10-80, or
10-86. It will be appreciated that N can be selected to encompass
similar, but higher order, ranges.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers provided in Table 21, wherein the individual is
classified as having lung cancer based on the at least one
biomarker value.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 21, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 21, wherein the individual is classified as
having lung cancer based on the biomarker values, and wherein
N=2-10.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from the group of biomarkers
set forth in Table 21, wherein the likelihood of the individual
having lung cancer is determined based on the biomarker values, and
wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers set forth in Table 21, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on the at least
one biomarker value.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 21, wherein the individual is
classified as having lung cancer, or the likelihood of the
individual having lung cancer is determined, based on said
biomarker values, wherein N=2-10.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, at least one
biomarker value corresponding to at least one biomarker selected
from the group of biomarkers set forth in Table 21, wherein the
individual is classified as not having lung cancer based on the at
least one biomarker value.
In another aspect, a method is provided for diagnosing that an
individual does not have lung cancer, the method including
detecting, in a biological sample from an individual, biomarker
values that each corresponding to one of at least N biomarkers
selected from the group of biomarkers set forth in Table 21,
wherein the individual is classified as not having lung cancer
based on the biomarker values, and wherein N=2-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 21, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-10.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of N biomarkers, wherein the biomarkers are selected from
the group of biomarkers set forth in Table 21, wherein a
classification of the biomarker values indicates that the
individual has lung cancer, and wherein N=3-15.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 21, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, and wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 21, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on the biomarker values, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 21,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-10.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 21,
wherein a classification of the biomarker values indicates an
absence of lung cancer in the individual, and wherein N=3-15.
In another aspect, a method is provided for diagnosing lung cancer
in an individual, the method including detecting, in a biological
sample from an individual, biomarker values that correspond to one
of at least N biomarkers selected from the group of biomarkers set
forth in Table 21, wherein the individual is classified as having
lung cancer based on a classification score that deviates from a
predetermined threshold, and wherein N=2-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 21, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-10.
In another aspect, a method is provided for screening smokers for
lung cancer, the method including detecting, in a biological sample
from an individual who is a smoker, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 21, wherein the individual is classified as having lung
cancer, or the likelihood of the individual having lung cancer is
determined, based on a classification score that deviates from a
predetermined threshold, wherein N=3-15.
In another aspect, a method is provided for diagnosing an absence
of lung cancer in an individual, the method including detecting, in
a biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 21, wherein said individual is
classified as not having lung cancer based on a classification
score that deviates from a predetermined threshold, and wherein
N=2-10.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises:
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises biomarker values that
each correspond to one of at least N biomarkers, wherein N is as
defined above, selected from the group of biomarkers set forth in
Table 21; performing with the computer a classification of each of
the biomarker values; and indicating a likelihood that the
individual has lung cancer based upon a plurality of
classifications.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises: retrieving on a computer biomarker
information for an individual, wherein the biomarker information
comprises biomarker values that each correspond to one of at least
N biomarkers selected from the group of biomarkers provided in
Table 21; performing with the computer a classification of each of
the biomarker values; and indicating whether the individual has
lung cancer based upon a plurality of classifications.
In another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers, wherein N is as defined above, in the biological sample
selected from the group of biomarkers set forth in Table 21; and
code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker values.
In another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises biomarker values that each correspond to one of at
least N biomarkers in the biological sample selected from the group
of biomarkers provided in Table 21; and code that executes a
classification method that indicates a lung cancer status of the
individual as a function of the biomarker values.
In another aspect, a computer-implemented method is provided for
indicating a likelihood of lung cancer. The method comprises
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises a biomarker value
corresponding to a biomarker selected from the group of biomarkers
set forth in Table 21; performing with the computer a
classification of the biomarker value; and indicating a likelihood
that the individual has lung cancer based upon the
classification.
In another aspect, a computer-implemented method is provided for
classifying an individual as either having or not having lung
cancer. The method comprises retrieving from a computer biomarker
information for an individual, wherein the biomarker information
comprises a biomarker value corresponding to a biomarker selected
from the group of biomarkers provided in Table 21; performing with
the computer a classification of the biomarker value; and
indicating whether the individual has lung cancer based upon the
classification.
In still another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises a
biomarker value corresponding to a biomarker in the biological
sample selected from the group of biomarkers set forth in Table 21;
and code that executes a classification method that indicates a
likelihood that the individual has lung cancer as a function of the
biomarker value.
In still another aspect, a computer program product is provided for
indicating a lung cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises a biomarker value corresponding to a biomarker in
the biological sample selected from the group of biomarkers
provided in Table 21; and code that executes a classification
method that indicates a lung cancer status of the individual as a
function of the biomarker value.
In one aspect of the application at least one of said N biomarkers
selected from Table 21 in each of the above methods is a biomarker
selected from the Table 20. In yet another embodiment said
biomarker selected from Table 20 is MMP-12.
In another aspect, a method is provided for diagnosing lung cancer,
the method including detecting, in a biological sample from an
individual, biomarker values that each correspond to a biomarker on
a panel of biomarkers selected from the group of panels set forth
in Tables 22-25 wherein a classification of the biomarker values
indicates that the individual has lung cancer.
In another aspect, a method is provided for diagnosing an absence
of lung cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of biomarkers selected from the group of
panels provided in Tables 22-25, wherein a classification of the
biomarker values indicates an absence of lung cancer in the
individual.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A is a flowchart for an exemplary method for detecting lung
cancer in a biological sample.
FIG. 1B is a flowchart for an exemplary method for detecting lung
cancer in a biological sample using a naive Bayes classification
method.
FIG. 2 shows a ROC curve for a single biomarker, SCFsR, using a
naive Bayes classifier for a test that detects lung cancer in
asymptomatic smokers.
FIG. 3 shows ROC curves for biomarker panels of from one to ten
biomarkers using naive Bayes classifiers for a test that detects
lung cancer in asymptomatic smokers.
FIG. 4 illustrates the increase in the classification score
(specificity+sensitivity) as the number of biomarkers is increased
from one to ten using naive Bayes classification for a benign
nodule-lung cancer panel.
FIG. 5 shows the measured biomarker distributions for SCFsR as a
cumulative distribution function (cdf) in log-transformed RFU for
the benign nodule control group (solid line) and the lung cancer
disease group (dotted line) along with their curve fits to a normal
cdf (dashed lines) used to train the naive Bayes classifiers
FIG. 6 illustrates an exemplary computer system for use with
various computer-implemented methods described herein.
FIG. 7 is a flowchart for a method of indicating the likelihood
that an individual has lung cancer in accordance with one
embodiment.
FIG. 8 is a flowchart for a method of indicating the likelihood
that an individual has lung cancer in accordance with one
embodiment.
FIG. 9 illustrates an exemplary aptamer assay that can be used to
detect one or more lung cancer biomarkers in a biological
sample.
FIG. 10 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
benign nodules from an aggregated set of potential biomarkers.
FIG. 11 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
asymptomatic smokers from an aggregated set of potential
biomarkers.
FIG. 12 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
benign nodules from a site-consistent set of potential
biomarkers.
FIG. 13 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
asymptomatic smokers from a site-consistent set of potential
biomarkers.
FIG. 14 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
benign nodules from a set of potential biomarkers resulting from a
combination of aggregated and site-consistent markers.
FIG. 15 shows a histogram of frequencies for which biomarkers were
used in building classifiers to distinguish between NSCLC and
asymptomatic smokers from a set of potential biomarkers resulting
from a combination of aggregated and site-consistent markers.
FIG. 16 shows gel images resulting from pull-down experiments that
illustrate the specificity of aptamers as capture reagents for the
proteins LBP, C9 and IgM. For each gel, lane 1 is the eluate from
the Streptavidin-agarose beads, lane 2 is the final eluate, and
lane is a MW marker lane (major bands are at 110, 50, 30, 15, and
3.5 kDa from top to bottom).
FIG. 17A shows a pair of histograms summarizing all possible single
protein naive Bayes classifier scores (sensitivity+specificity)
using the biomarkers set forth in Table 1, Col 5 (solid) and a set
of random markers (dotted).
FIG. 17B shows a pair of histograms summarizing all possible
two-protein protein naive Bayes classifier scores
(sensitivity+specificity) using the biomarkers set forth in Table
1, Col 5 (solid) and a set of random markers (dotted).
FIG. 17C shows a pair of histograms summarizing all possible
three-protein naive Bayes classifier scores
(sensitivity+specificity) using the biomarkers set forth in Table
1, Col 5 (solid) and a set of random markers (dotted).
FIG. 18A shows a pair of histograms summarizing all possible single
protein naive Bayes classifier scores (sensitivity+specificity)
using the biomarkers set forth in Table 1, Col 6 (solid) and a set
of random markers (dotted).
FIG. 18B shows a pair of histograms summarizing all possible
two-protein protein naive Bayes classifier scores
(sensitivity+specificity) using the biomarkers set forth in Table
1, Col 6 (solid) and a set of random markers (dotted).
FIG. 18C shows a pair of histograms summarizing all possible
three-protein naive Bayes classifier scores
(sensitivity+specificity) using the biomarkers set forth in Table
1, Col 6 (solid) and a set of random markers (dotted).
FIG. 19A shows the sensitivity+specificity score for naive Bayes
classifiers using from 2-10 markers selected from the full panel
(.diamond-solid.) and the scores obtained by dropping the best 5
(.box-solid.), 10 (.tangle-solidup.) and 15 (x) markers during
classifier generation for the benign nodule control group.
FIG. 19B shows the sensitivity+specificity score for naive Bayes
classifiers using from 2-10 markers selected from the full panel
(.diamond-solid.) and the scores obtained by dropping the best 5
(.box-solid.), 10 (.tangle-solidup.) and 15 (x) markers during
classifier generation for the smoker control group.
FIG. 20A shows a set of ROC curves modeled from the data in Tables
38 and 39 for panels of from one to five markers.
FIG. 20B shows a set of ROC curves computed from the training data
for panels of from one to five markers as in FIG. 19A.
FIGS. 21A-21C show relative changes in protein expression for 813
proteins from eight NSCLC resection samples between adjacent and
distant tissue (FIG. 21A), tumor and adjacent tissue (FIG. 21B) and
tumor and distant tissue (FIG. 21C) expressed as log 2 median
ratios. The dotted line represents a two-fold change (log 2=1).
FIG. 22 show a heat map of protein levels in tumor tissue samples.
The samples are arranged in columns and are separated into distant,
adjacent, and tumor samples. Within each tissue type, the samples
are separated into adenocarcinoma (AC) and squamous cell carcinoma
(SCC). The numbers above each column correspond to patient codes.
The proteins are displayed in rows and were ordered using
hierarchial clustering.
FIGS. 23A-23T) depict proteins with increased levels in tumor
tissue compared with adjacent or distal tissue.
FIGS. 24A-24P depict proteins with decreased levels in tumor tissue
compared with adjacent or distal tissue from the eight NSCLC
samples used in this study.
FIG. 25 shows SOMAmer histochemistry on frozen tissue sections for
selected biomarkers detected in this study. (A) Thrombospondin-2
(red) staining the fibrocollagenous matrix surrounding a tumor
nest. (B) Corresponding normal lung specimen stained with
Thrombospondin-2 SOMAmer (red). (C) Macrophage Mannose Receptor
SOMAmer (red) staining scattered macrophages in a lung
adenocarcinoma. (D) Macrophage Mannose Receptor SOMAmer (red)
staining numerous alveolar macrophages in a section of normal lung
parenchyma. (E) Multicolor image highlighting the cytomorphologic
distribution of Macrophage Mannose Receptor SOMAmer staining:
Green=Cytokeratin (AE1/AE3 antibody), Red=CD31 (EP3095 Antibody),
and Orange=SOMAmer. All nuclei in this figure are counterstained
with DAPI.
FIGS. 26A-26F show changes in protein expression in NSCLC tissue
compared to serum. The top two panels show the log 2 ratio (LR)
derived from serum samples versus log ratios derived from adjacent
tissue and distant tissue, respectively. The bottom four panels
feature zoomed portions of plots above, indicated by the color of
the plot (green for decreased and red for increased expression
compared to non-tumor tissue). Analytes shown in FIGS. 23 and 24
have been labeled and analytes mentioned in the publication on the
serum samples are shown in filled red symbols red.
FIG. 27 depicts thrombospondin-2 histochemical identification in
tissue samples. Thrombospondin-2 is identified in a serial frozen
section of a single lung carcinoma specimen by (A) a home-made
rabbit polyclonal thrombospondin-2 polyclonal antibody, (B) the
pre-immune serum from rabbits used to make the home-made polyclonal
antibody, (C) a commercial (Novus) rabbit polyclonal
thrombospondin-2 antibody, and (D) the thrombospondin-2 SOMAmer.
The thrombospondin-2 SOMAmer was then used to stain frozen sections
of normal and malignant lung tissue, with standard
Avidin-Biotin-Peroxidase color development, to demonstrate
different morphologic distributions: (E) Strong staining of the
fibrotic stroma surrounding tumor nests, with minimal cytosolic
staining of carcinoma cells, (F) Strong staining of the fibrotic
stroma surrounding tumor nests in a mucinous adenocarcinoma, with
no significant staining of the carcinoma cells, (G) normal lung
tissue, showing strong cytosolic staining of bronchial epithelium
and scattered alveolar macrophages, and (H) strong cytosolic
staining of an adenocarcinoma, with no significant staining of the
non-fibrotic, predominantly inflammatory stroma.
DETAILED DESCRIPTION
The practice of the invention disclosed herein employs, unless
otherwise indicated, conventional methods of chemistry,
microbiology, molecular biology, and recombinant DNA techniques
within the level of skill in the art. Such techniques are explained
fully in the literature. See, e.g., Sambrook, et al. Molecular
Cloning: A Laboratory Manual (Current Edition); DNA Cloning: A
Practical Approach, vol. I & II (D. Glover, ed.);
Oligonucleotide Synthesis (N. Gait, ed., Current Edition); Nucleic
Acid Hybridization (B. Hames & S. Higgins, eds., Current
Edition); Transcription and Translation (B. Hames & S. Higgins,
eds., Current Edition; Histology for Pathologists (S. E. Mills,
Current Edition). All publications, published patent documents, and
patent applications cited in this specification are indicative of
the level of skill in the art(s) to which the invention pertains.
All publications, published patent documents, and patent
applications cited herein are hereby incorporated by reference to
the same extent as though each individual publication, published
patent document, or patent application was specifically and
individually indicated as being incorporated by reference.
Reference will now be made in detail to representative embodiments
of the invention. While the invention will be described in
conjunction with the enumerated embodiments, it will be understood
that the invention is not intended to be limited to those
embodiments. On the contrary, the invention is intended to cover
all alternatives, modifications, and equivalents that may be
included within the scope of the present invention as defined by
the claims.
Unless defined otherwise, technical and scientific terms used
herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Although
any methods, devices, and materials similar or equivalent to those
described herein can be used in the practice or testing of the
invention, the preferred methods, devices and materials are now
described.
As used in this application, including the appended claims, the
singular forms "a," "an," and "the" include plural references,
unless the content clearly dictates otherwise, and are used
interchangeably with "at least one" and "one or more." Thus,
reference to "an aptamer" includes mixtures of aptamers, reference
to "a probe" includes mixtures of probes, and the like.
As used herein, the term "about" represents an insignificant
modification or variation of the numerical value such that the
basic function of the item to which the numerical value relates is
unchanged.
As used herein, the terms "comprises," "comprising," "includes,"
"including," "contains," "containing," and any variations thereof,
are intended to cover a non-exclusive inclusion, such that a
process, method, product-by-process, or composition of matter that
comprises, includes, or contains an element or list of elements
does not include only those elements but may include other elements
not expressly listed or inherent to such process, method,
product-by-process, or composition of matter.
The present application includes biomarkers, methods, devices,
reagents, systems, and kits for the detection and diagnosis of lung
cancer.
In one aspect, one or more biomarkers are provided for use either
alone or in various combinations to diagnose lung cancer, permit
the differential diagnosis of pulmonary nodules as benign or
malignant, monitor lung cancer recurrence, or address other
clinical indications. In other aspects said biomarker(s) can be
used in determining information about lung cancer in an individual
such as, prognosis, cancer classification, prediction of disease
risk or selection of treatment. As described in detail below,
exemplary embodiments include the biomarkers provided in Tables 18,
20 and 21, which were identified using a multiplex aptamer-based
assay that is described generally in Example 1 and more
specifically in Examples 2 and 6. Each of the biomarkers is useful
in assaying any type of sample as defined below.
Table 1, Col. 2 sets forth the findings obtained from analyzing
hundreds of individual blood samples from NSCLC cancer cases, and
hundreds of equivalent individual blood samples from smokers and
from individuals diagnosed with benign lung nodules. The smoker and
benign nodule groups were designed to match the populations with
which a lung cancer diagnostic test can have the most benefit.
(These cases and controls were obtained from multiple clinical
sites to mimic the range of real world conditions under which such
a test can be applied). The potential biomarkers were measured in
individual samples rather than pooling the disease and control
blood; this allowed a better understanding of the individual and
group variations in the phenotypes associated with the presence and
absence of disease (in this case lung cancer). Since over 800
protein measurements were made on each sample, and several hundred
samples from each of the disease and the control populations were
individually measured, Table 1, Col. 2 resulted from an analysis of
an uncommonly large set of data. The measurements were analyzed
using the methods described in the section, "Classification of
Biomarkers and Calculation of Disease Scores" herein.
Table 1, Col. 2 lists the biomarkers found to be useful in
distinguishing samples obtained from individuals with NSCLC from
"control" samples obtained from smokers and individuals with benign
lung nodules. Using a multiplex aptamer assay as described herein,
thirty-eight biomarkers were discovered that distinguished the
samples obtained from individuals who had lung cancer from the
samples obtained from individuals in the smoker control group (see
Table 1, Col. 6). Similarly, using a multiplex aptamer assay, forty
biomarkers were discovered that distinguished samples obtained from
individuals with NSCLC from samples obtained from people who had
benign lung nodules (see Table 1, Col. 5). Together, the two lists
of 38 and 40 biomarkers are comprised of 61 unique biomarkers,
because there is considerable overlap between the list of
biomarkers for distinguishing NSCLC from benign nodules and the
list for distinguishing NSCLC from smokers who do not have lung
cancer.
Table 18 sets forth the findings obtained from analyzing eight
individual tissue samples of smokers diagnosed with NSCLC as
described in Example 6. All of the patients were smokers ranging
from 47 to 75 years old and covering NSCLC stages 1A through 3B.
Three samples were obtained from each individual: tumor tissue,
adjacent healthy tissue (within 1 cm of the tumor) and distant
uninvolved lung tissue. The samples were chosen to match the
populations with which a lung cancer diagnostic test can have the
most benefit. The potential biomarkers were measured in individual
samples rather than pooling the disease and control tissue; this
allowed a better understanding of the individual and group
variations in the phenotypes associated with the presence and
absence of disease (in this case lung cancer). The measurements
were analyzed using the Mann-Whitney test.
Table 18 lists the biomarkers found to be useful in distinguishing
samples obtained from individuals with NSCLC from "control" samples
obtained from adjacent and distal uninvolved lung tissue obtained
from the same individuals. Using a multiplex aptamer assay as
described herein, thirty-six biomarkers were discovered that
distinguished the tumor tissue samples from samples obtained from
adjacent and distal lung tissue in individuals who had been
diagnosed with NSCLC. With reference to Table 1, col. 2, it can be
seen that eleven of the biomarkers overlap those identified in
serum samples as described in Example 2. An additional marker which
was not measured in the original serum profiling, MMP-12, has since
been found to be a useful biomarker in both serum and in tissue.
Table 21 provides a list of the total number of biomarkers
(eighty-six) identified in both the serum and tumor tissue samples
combined. Table 20 provides a list of the biomarkers identified
which were unique to the tumor tissue samples (twenty-five).
While certain of the described lung cancer biomarkers are useful
alone for detecting and diagnosing lung cancer, methods are also
described herein for the grouping of multiple subsets of the lung
cancer biomarkers, where each grouping or subset selection is
useful as a panel of three or more biomarkers, interchangeably
referred to herein as a "biomarker panel" and a panel. Thus,
various embodiments of the instant application provide combinations
comprising N biomarkers, wherein N is at least two biomarkers. In
other embodiments, N is selected from 2-86 biomarkers (Table 21);
2-36 biomarkers (Table 18) or 2-25 biomarkers (Table 20). In other
embodiments, N is selected from 2-86 (Table 21) and at least one of
said N biomarkers is MMP-12. In other embodiments, N is selected
from 2-25 (Table 20) and at least one of said N biomarkers is
MMP-12. Representative panels of 2-5 biomarkers including MMP-12 as
one of the markers are set forth in Tables 22-25.
In yet other embodiments, the biomarkers are selected from those
listed in Table 18 and N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25, 2-30, 2-36. In other embodiments, N is
selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30,
3-36. In other embodiments, N is selected to be any number from
4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-36. In other embodiments, N is
selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30,
5-36. In other embodiments, N is selected to be any number from
6-10, 6-15, 6-20, 6-25, 6-30, 6-36. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-36.
In other embodiments, N is selected to be any number from 8-10,
8-15, 8-20, 8-25, 8-30, 8-36. In other embodiments, N is selected
to be any number from 9-15, 9-20, 9-25, 9-30, 9-36. In other
embodiments, N is selected to be any number from 10-15, 10-20,
10-25, 10-30, 10-36. It will be appreciated that N can be selected
to encompass similar, but higher order, ranges.
In yet other embodiments the biomarkers are selected from those
listed in Table 20 and N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25. In other embodiments, N is selected to be
any number from 3-7, 3-10, 3-15, 3-20, 3-25. In other embodiments,
N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25. In
other embodiments, N is selected to be any number from 5-7, 5-10,
5-15, 5-20, 5-25. In other embodiments, N is selected to be any
number from 6-10, 6-15, 6-20, 6-25. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25. In other
embodiments, N is selected to be any number from 8-10, 8-15, 8-20,
8-25. In other embodiments, N is selected to be any number from
9-15, 9-20, 9-25. In other embodiments, N is selected to be any
number from 10-15, 10-20, 10-25. In other embodiments, N is
selected to be any number from 9-15, 9-20, 9-25. In other
embodiments, N is selected to be any number from 10-15, 10-20,
10-25. It will be appreciated that N can be selected to encompass
similar, but higher order, ranges.
In yet other embodiments the biomarkers are selected from those
listed in Table 21 and N is selected to be any number from 2-7,
2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50, 2-55, 2-60,
2-65, 2-70, 2-75, 2-80, or 2-86. In other embodiments, N is
selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30,
3-35, 3-40, 3-45, 3-50, 3-55, 3-60, 3-65, 3-70, 3-75, 3-80, or
3-86. In other embodiments, N is selected to be any number from
4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50, 4-55,
4-60, 4-65, 4-70, 4-75, 4-80, or 4-86. In other embodiments, N is
selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30,
5-35, 5-40, 5-45, 5-50, 5-55, 5-60, 5-65, 5-70, 5-75, 5-80, or
5-86. In other embodiments, N is selected to be any number from
6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, 6-60,
6-65, 6-70, 6-75, 6-80, or 6-86. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35,
7-40, 7-45, 7-50, 7-55, 7-60, 7-65, 7-70, 7-75, 7-80, or 7-86. In
other embodiments, N is selected to be any number from 8-10, 8-15,
8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, 8-60, 8-65, 8-70,
8-75, 8-80, or 8-86. In other embodiments, N is selected to be any
number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55,
9-60, 9-65, 9-70, 9-75, 9-80, or 9-86. In other embodiments, N is
selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35,
10-40, 10-45, 10-50, 10-55, 10-60, 10-65, 10-70, 10-75, 10-80, or
10-86. It will be appreciated that N can be selected to encompass
similar, but higher order, ranges.
In one embodiment, the number of biomarkers useful for a biomarker
subset or panel is based on the sensitivity and specificity value
for the particular combination of biomarker values. The terms
"sensitivity" and "specificity" are used herein with respect to the
ability to correctly classify an individual, based on one or more
biomarker values detected in their biological sample, as having
lung cancer or not having lung cancer. "Sensitivity" indicates the
performance of the biomarker(s) with respect to correctly
classifying individuals that have lung cancer. "Specificity"
indicates the performance of the biomarker(s) with respect to
correctly classifying individuals who do not have lung cancer. For
example, 85% specificity and 90% sensitivity for a panel of markers
used to test a set of control samples and lung cancer samples
indicates that 85% of the control samples were correctly classified
as control samples by the panel, and 90% of the lung cancer samples
were correctly classified as lung cancer samples by the panel. The
desired or preferred minimum value can be determined as described
in Example 3.
In one aspect, lung cancer is detected or diagnosed in an
individual by conducting an assay on a biological sample from the
individual and detecting biomarker values that each correspond to
at least one of the biomarkers MMP-7, MMP-12, or IGFBP-2 and at
least N additional biomarkers selected from the list of biomarkers
in Table 21, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14 or 15. In a further aspect, lung cancer is detected or
diagnosed in an individual by conducting an assay on a biological
sample from the individual and detecting biomarker values that each
correspond to the biomarkers MMP-7, MMP-12, or IGFBP-2 and one of
at least N additional biomarkers selected from the list of
biomarkers in Table 21, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12 or 13. In a further aspect, lung cancer is detected or
diagnosed in an individual by conducting an assay on a biological
sample from the individual and detecting biomarker values that each
correspond to the biomarker MMP-7 and one of at least N additional
biomarkers selected from the list of biomarkers in Table 21,
wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
In a further aspect, lung cancer is detected or diagnosed in an
individual by conducting an assay on a biological sample from the
individual and detecting biomarker values that each correspond to
the biomarker MMP-12 and one of at least N additional biomarkers
selected from the list of biomarkers in Table 21, wherein N equals
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15. In a further
aspect, lung cancer is detected or diagnosed in an individual by
conducting an assay on a biological sample from the individual and
detecting biomarker values that each correspond to the biomarker
IGFBP-2 and one of at least N additional biomarkers selected from
the list of biomarkers in Table 21, wherein N equals 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14 or 15.
The lung cancer biomarkers identified herein represent a relatively
large number of choices for subsets or panels of biomarkers that
can be used to effectively detect or diagnose lung cancer.
Selection of the desired number of such biomarkers depends on the
specific combination of biomarkers chosen. It is important to
remember that panels of biomarkers for detecting or diagnosing lung
cancer may also include biomarkers not found in Tables 18, 20 or
21, and that the inclusion of additional biomarkers not found in
Tables 18, 20 or 21 may reduce the number of biomarkers in the
particular subset or panel that is selected from Tables 18, 20 or
21. The number of biomarkers from Tables 18, 20 or 21 used in a
subset or panel may also be reduced if additional biomedical
information is used in conjunction with the biomarker values to
establish acceptable sensitivity and specificity values for a given
assay.
Another factor that can affect the number of biomarkers to be used
in a subset or panel of biomarkers is the procedures used to obtain
biological samples from individuals who are being diagnosed for
lung cancer. In a carefully controlled sample procurement
environment, the number of biomarkers necessary to meet desired
sensitivity and specificity values will be lower than in a
situation where there can be more variation in sample collection,
handling and storage. In developing the list of biomarkers set
forth in Tables 18, 20 or 21, multiple sample collection sites were
utilized to collect data for classifier training. This provides for
more robust biomarkers that are less sensitive to variations in
sample collection, handling and storage, but can also require that
the number of biomarkers in a subset or panel be larger than if the
training data were all obtained under very similar conditions.
One aspect of the instant application can be described generally
with reference to FIGS. 1A and B. A biological sample is obtained
from an individual or individuals of interest. The biological
sample is then assayed to detect the presence of one or more (N)
biomarkers of interest and to determine a biomarker value for each
of said N biomarkers (referred to in FIG. 1B as marker RFU). Once a
biomarker has been detected and a biomarker value assigned each
marker is scored or classified as described in detail herein. The
marker scores are then combined to provide a total diagnostic
score, which indicates the likelihood that the individual from whom
the sample was obtained has lung cancer.
As used herein, "lung" may be interchangeably referred to as
"pulmonary".
As used herein, "smoker" refers to an individual who has a history
of tobacco smoke inhalation.
"Biological sample", "sample", and "test sample" are used
interchangeably herein to refer to any material, biological fluid,
tissue, or cell obtained or otherwise derived from an individual.
This includes blood (including whole blood, leukocytes, peripheral
blood mononuclear cells, buffy coat, plasma, and serum), sputum,
tears, mucus, nasal washes, nasal aspirate, breath, urine, semen,
saliva, meningeal fluid, amniotic fluid, glandular fluid, lymph
fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint
aspirate, cells, a cellular extract, and cerebrospinal fluid. This
also includes experimentally separated fractions of all of the
preceding. For example, a blood sample can be fractionated into
serum or into fractions containing particular types of blood cells,
such as red blood cells or white blood cells (leukocytes). If
desired, a sample can be a combination of samples from an
individual, such as a combination of a tissue and fluid sample. The
term "biological sample" also includes materials containing
homogenized solid material, such as from a stool sample, a tissue
sample, or a tissue biopsy, for example. The term "biological
sample" also includes materials derived from a tissue culture or a
cell culture. Any suitable methods for obtaining a biological
sample can be employed; exemplary methods include, e.g.,
phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate
biopsy procedure. Exemplary tissues susceptible to fine needle
aspiration include lymph node, lung, lung washes, BAL
(bronchoalveolar lavage), thyroid, breast, and liver. Samples can
also be collected, e.g., by micro dissection (e.g., laser capture
micro dissection (LCM) or laser micro dissection (LMD)), bladder
wash, smear (e.g., a PAP smear), or ductal lavage. A "biological
sample" obtained or derived from an individual includes any such
sample that has been processed in any suitable manner after being
obtained from the individual.
A "Tissue sample" or "Tissue" refers to a certain subset of the
biological samples described above. According to this definition,
tissues are collections of macromolecules in a heterogeneous
environment. As used herein, tissue refers to a single cell type, a
collection of cell types, an aggregate of cells, or an aggregate of
macromolecules. Tissues are generally a physical array of
macromolecules that can be either fluid or rigid, both in terms of
structure and composition. Extracellular matrix is an example of a
more rigid tissue, both structurally and compositionally, while a
membrane bilayer is more fluid in structure and composition. Tissue
includes, but is not limited to, an aggregate of cells usually of a
particular kind together with their intercellular substance that
form one of the structural materials commonly used to denote the
general cellular fabric of a given organ, e.g., kidney tissue,
brain tissue, lung tissue. The four general classes of tissues are
epithelial tissue, connective tissue, nerve tissue, and muscle
tissue. Methods for identifying slow off-rate aptamers to tissue
targets are described in International Application Pub. No. WO
2011/006075, published Jan. 13, 2011, which is incorporated herein
by reference in its entirety.
Examples of tissues which fall within this definition include, but
are not limited to, heterogeneous aggregates of macromolecules such
as fibrin clots which are acellular; homogeneous or heterogeneous
aggregates of cells; higher ordered structures containing cells
which have a specific function, such as organs, tumors, lymph
nodes, arteries, etc.; and individual cells. Tissues or cells can
be in their natural environment, isolated, or in tissue culture.
The tissue can be intact or modified. The modification can include
numerous changes such as transformation, transfection, activation,
and substructure isolation, e.g., cell membranes, cell nuclei, cell
organelles, etc.
Sources of the tissue, cell or subcellular structures can be
obtained from prokaryotes as well as eukaryotes. This includes
human, animal, plant, bacterial, fungal and viral structures.
Further, it should be realized that a biological sample can be
derived by taking biological samples from a number of individuals
and pooling them or pooling an aliquot of each individual's
biological sample. The pooled sample can be treated as a sample
from a single individual and if the presence of cancer is
established in the pooled sample, then each individual biological
sample can be re-tested to determine which individual/s have lung
cancer.
For purposes of this specification, the phrase "data attributed to
a biological sample from an individual" is intended to mean that
the data in some form derived from, or were generated using, the
biological sample of the individual. The data may have been
reformatted, revised, or mathematically altered to some degree
after having been generated, such as by conversion from units in
one measurement system to units in another measurement system; but,
the data are understood to have been derived from, or were
generated using, the biological sample.
"Target", "target molecule", and "analyte" are used interchangeably
herein to refer to any molecule of interest that may be present in
a biological sample. A "molecule of interest" includes any minor
variation of a particular molecule, such as, in the case of a
protein, for example, minor variations in amino acid sequence,
disulfide bond formation, glycosylation, lipidation, acetylation,
phosphorylation, or any other manipulation or modification, such as
conjugation with a labeling component, which does not substantially
alter the identity of the molecule. A "target molecule", "target",
or "analyte" is a set of copies of one type or species of molecule
or multi-molecular structure. "Target molecules", "targets", and
"analytes" refer to more than one such set of molecules. Exemplary
target molecules include proteins, polypeptides, nucleic acids,
carbohydrates, lipids, polysaccharides, glycoproteins, hormones,
receptors, antigens, antibodies, affybodies, antibody mimics,
viruses, pathogens, toxic substances, substrates, metabolites,
transition state analogs, cofactors, inhibitors, drugs, dyes,
nutrients, growth factors, cells, tissues, and any fragment or
portion of any of the foregoing.
As used herein, "polypeptide," "peptide," and "protein" are used
interchangeably herein to refer to polymers of amino acids of any
length. The polymer may be linear or branched, it may comprise
modified amino acids, and it may be interrupted by non-amino acids.
The terms also encompass an amino acid polymer that has been
modified naturally or by intervention; for example, disulfide bond
formation, glycosylation, lipidation, acetylation, phosphorylation,
or any other manipulation or modification, such as conjugation with
a labeling component. Also included within the definition are, for
example, polypeptides containing one or more analogs of an amino
acid (including, for example, unnatural amino acids, etc.), as well
as other modifications known in the art. Polypeptides can be single
chains or associated chains. Also included within the definition
are preproteins and intact mature proteins; peptides or
polypeptides derived from a mature protein; fragments of a protein;
splice variants; recombinant forms of a protein; protein variants
with amino acid modifications, deletions, or substitutions;
digests; and post-translational modifications, such as
glycosylation, acetylation, phosphorylation, and the like.
As used herein, "marker" and "biomarker" are used interchangeably
to refer to a target molecule that indicates or is a sign of a
normal or abnormal process in an individual or of a disease or
other condition in an individual. More specifically, a "marker" or
"biomarker" is an anatomic, physiologic, biochemical, or molecular
parameter associated with the presence of a specific physiological
state or process, whether normal or abnormal, and, if abnormal,
whether chronic or acute. Biomarkers are detectable and measurable
by a variety of methods including laboratory assays and medical
imaging. When a biomarker is a protein, it is also possible to use
the expression of the corresponding gene as a surrogate measure of
the amount or presence or absence of the corresponding protein
biomarker in a biological sample or methylation state of the gene
encoding the biomarker or proteins that control expression of the
biomarker.
As used herein, "biomarker value", "value", "biomarker level", and
"level" are used interchangeably to refer to a measurement that is
made using any analytical method for detecting the biomarker in a
biological sample and that indicates the presence, absence,
absolute amount or concentration, relative amount or concentration,
titer, a level, an expression level, a ratio of measured levels, or
the like, of, for, or corresponding to the biomarker in the
biological sample. The exact nature of the "value" or "level"
depends on the specific design and components of the particular
analytical method employed to detect the biomarker.
When a biomarker indicates or is a sign of an abnormal process or a
disease or other condition in an individual, that biomarker is
generally described as being either over-expressed or
under-expressed as compared to an expression level or value of the
biomarker that indicates or is a sign of a normal process or an
absence of a disease or other condition in an individual.
"Up-regulation", "up-regulated", "over-expression",
"over-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
"Down-regulation", "down-regulated", "under-expression",
"under-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
Further, a biomarker that is either over-expressed or
under-expressed can also be referred to as being "differentially
expressed" or as having a "differential level" or "differential
value" as compared to a "normal" expression level or value of the
biomarker that indicates or is a sign of a normal process or an
absence of a disease or other condition in an individual. Thus,
"differential expression" of a biomarker can also be referred to as
a variation from a "normal" expression level of the biomarker.
The term "differential gene expression" and "differential
expression" are used interchangeably to refer to a gene (or its
corresponding protein expression product) whose expression is
activated to a higher or lower level in a subject suffering from a
specific disease, relative to its expression in a normal or control
subject. The terms also include genes (or the corresponding protein
expression products) whose expression is activated to a higher or
lower level at different stages of the same disease. It is also
understood that a differentially expressed gene may be either
activated or inhibited at the nucleic acid level or protein level,
or may be subject to alternative splicing to result in a different
polypeptide product. Such differences may be evidenced by a variety
of changes including mRNA levels, surface expression, secretion or
other partitioning of a polypeptide. Differential gene expression
may include a comparison of expression between two or more genes or
their gene products; or a comparison of the ratios of the
expression between two or more genes or their gene products; or
even a comparison of two differently processed products of the same
gene, which differ between normal subjects and subjects suffering
from a disease; or between various stages of the same disease.
Differential expression includes both quantitative, as well as
qualitative, differences in the temporal or cellular expression
pattern in a gene or its expression products among, for example,
normal and diseased cells, or among cells which have undergone
different disease events or disease stages.
As used herein, "individual" refers to a test subject or patient.
The individual can be a mammal or a non-mammal. In various
embodiments, the individual is a mammal. A mammalian individual can
be a human or non-human. In various embodiments, the individual is
a human. A healthy or normal individual is an individual in which
the disease or condition of interest (including, for example, lung
diseases, lung-associated diseases, or other lung conditions) is
not detectable by conventional diagnostic methods.
"Diagnose", "diagnosing", "diagnosis", and variations thereof refer
to the detection, determination, or recognition of a health status
or condition of an individual on the basis of one or more signs,
symptoms, data, or other information pertaining to that individual.
The health status of an individual can be diagnosed as
healthy/normal (i.e., a diagnosis of the absence of a disease or
condition) or diagnosed as ill/abnormal (i.e., a diagnosis of the
presence, or an assessment of the characteristics, of a disease or
condition). The terms "diagnose", "diagnosing", "diagnosis", etc.,
encompass, with respect to a particular disease or condition, the
initial detection of the disease; the characterization or
classification of the disease; the detection of the progression,
remission, or recurrence of the disease; and the detection of
disease response after the administration of a treatment or therapy
to the individual. The diagnosis of lung cancer includes
distinguishing individuals, including smokers and nonsmokers, who
have cancer from individuals who do not. It further includes
distinguishing benign pulmonary nodules from cancerous pulmonary
nodules.
"Prognose", "prognosing", "prognosis", and variations thereof refer
to the prediction of a future course of a disease or condition in
an individual who has the disease or condition (e.g., predicting
patient survival), and such terms encompass the evaluation of
disease response after the administration of a treatment or therapy
to the individual.
"Evaluate", "evaluating", "evaluation", and variations thereof
encompass both "diagnose" and "prognose" and also encompass
determinations or predictions about the future course of a disease
or condition in an individual who does not have the disease as well
as determinations or predictions regarding the likelihood that a
disease or condition will recur in an individual who apparently has
been cured of the disease. The term "evaluate" also encompasses
assessing an individual's response to a therapy, such as, for
example, predicting whether an individual is likely to respond
favorably to a therapeutic agent or is unlikely to respond to a
therapeutic agent (or will experience toxic or other undesirable
side effects, for example), selecting a therapeutic agent for
administration to an individual, or monitoring or determining an
individual's response to a therapy that has been administered to
the individual. Thus, "evaluating" lung cancer can include, for
example, any of the following: prognosing the future course of lung
cancer in an individual; predicting the recurrence of lung cancer
in an individual who apparently has been cured of lung cancer; or
determining or predicting an individual's response to a lung cancer
treatment or selecting a lung cancer treatment to administer to an
individual based upon a determination of the biomarker values
derived from the individual's biological sample.
Any of the following examples may be referred to as either
"diagnosing" or "evaluating" lung cancer: initially detecting the
presence or absence of lung cancer; determining a specific stage,
type or sub-type, or other classification or characteristic of lung
cancer; determining whether a pulmonary nodule is a benign lesion
or a malignant lung tumor; or detecting/monitoring lung cancer
progression (e.g., monitoring lung tumor growth or metastatic
spread), remission, or recurrence.
As used herein, "additional biomedical information" refers to one
or more evaluations of an individual, other than using any of the
biomarkers described herein, that are associated with lung cancer
risk. "Additional biomedical information" includes any of the
following: physical descriptors of an individual, physical
descriptors of a pulmonary nodule observed by CT imaging, the
height and/or weight of an individual, the gender of an individual,
the ethnicity of an individual, smoking history, occupational
history, exposure to known carcinogens (e.g., exposure to any of
asbestos, radon gas, chemicals, smoke from fires, and air
pollution, which can include emissions from stationary or mobile
sources such as industrial/factory or auto/marine/aircraft
emissions), exposure to second-hand smoke, family history of lung
cancer (or other cancer), the presence of pulmonary nodules, size
of nodules, location of nodules, morphology of nodules (e.g., as
observed through CT imaging, ground glass opacity (GGO), solid,
non-solid), edge characteristics of the nodule (e.g., smooth,
lobulated, sharp and smooth, spiculated, infiltrating), and the
like. Smoking history is usually quantified in terms of "pack
years", which refers to the number of years a person has smoked
multiplied by the average number of packs smoked per day. For
example, a person who has smoked, on average, one pack of
cigarettes per day for 35 years is referred to as having 35 pack
years of smoking history. Additional biomedical information can be
obtained from an individual using routine techniques known in the
art, such as from the individual themselves by use of a routine
patient questionnaire or health history questionnaire, etc., or
from a medical practitioner, etc. Alternately, additional
biomedical information can be obtained from routine imaging
techniques, including CT imaging (e.g., low-dose CT imaging) and
X-ray. Testing of biomarker levels in combination with an
evaluation of any additional biomedical information may, for
example, improve sensitivity, specificity, and/or AUC for detecting
lung cancer (or other lung cancer-related uses) as compared to
biomarker testing alone or evaluating any particular item of
additional biomedical information alone (e.g., CT imaging
alone).
The term "area under the curve" or "AUC" refers to the area under
the curve of a receiver operating characteristic (ROC) curve, both
of which are well known in the art. AUC measures are useful for
comparing the accuracy of a classifier across the complete data
range. Classifiers with a greater AUC have a greater capacity to
classify unknowns correctly between two groups of interest (e.g.,
lung cancer samples and normal or control samples). ROC curves are
useful for plotting the performance of a particular feature (e.g.,
any of the biomarkers described herein and/or any item of
additional biomedical information) in distinguishing between two
populations (e.g., cases having lung cancer and controls without
lung cancer). Typically, the feature data across the entire
population (e.g., the cases and controls) are sorted in ascending
order based on the value of a single feature. Then, for each value
for that feature, the true positive and false positive rates for
the data are calculated. The true positive rate is determined by
counting the number of cases above the value for that feature and
then dividing by the total number of cases. The false positive rate
is determined by counting the number of controls above the value
for that feature and then dividing by the total number of controls.
Although this definition refers to scenarios in which a feature is
elevated in cases compared to controls, this definition also
applies to scenarios in which a feature is lower in cases compared
to the controls (in such a scenario, samples below the value for
that feature would be counted). ROC curves can be generated for a
single feature as well as for other single outputs, for example, a
combination of two or more features can be mathematically combined
(e.g., added, subtracted, multiplied, etc.) to provide a single sum
value, and this single sum value can be plotted in a ROC curve.
Additionally, any combination of multiple features, in which the
combination derives a single output value, can be plotted in a ROC
curve. These combinations of features may comprise a test. The ROC
curve is the plot of the true positive rate (sensitivity) of a test
against the false positive rate (1-specificity) of the test.
As used herein, "detecting" or "determining" with respect to a
biomarker value includes the use of both the instrument required to
observe and record a signal corresponding to a biomarker value and
the material/s required to generate that signal. In various
embodiments, the biomarker value is detected using any suitable
method, including fluorescence, chemiluminescence, surface plasmon
resonance, surface acoustic waves, mass spectrometry, infrared
spectroscopy, Raman spectroscopy, atomic force microscopy, scanning
tunneling microscopy, electrochemical detection methods, nuclear
magnetic resonance, quantum dots, and the like.
"Solid support" refers herein to any substrate having a surface to
which molecules may be attached, directly or indirectly, through
either covalent or non-covalent bonds. A "solid support" can have a
variety of physical formats, which can include, for example, a
membrane; a chip (e.g., a protein chip); a slide (e.g., a glass
slide or coverslip); a column; a hollow, solid, semi-solid, pore-
or cavity-containing particle, such as, for example, a bead; a gel;
a fiber, including a fiber optic material; a matrix; and a sample
receptacle. Exemplary sample receptacles include sample wells,
tubes, capillaries, vials, and any other vessel, groove or
indentation capable of holding a sample. A sample receptacle can be
contained on a multi-sample platform, such as a microtiter plate,
slide, microfluidics device, and the like. A support can be
composed of a natural or synthetic material, an organic or
inorganic material. The composition of the solid support on which
capture reagents are attached generally depends on the method of
attachment (e.g., covalent attachment). Other exemplary receptacles
include microdroplets and microfluidic controlled or bulk
oil/aqueous emulsions within which assays and related manipulations
can occur. Suitable solid supports include, for example, plastics,
resins, polysaccharides, silica or silica-based materials,
functionalized glass, modified silicon, carbon, metals, inorganic
glasses, membranes, nylon, natural fibers (such as, for example,
silk, wool and cotton), polymers, and the like. The material
composing the solid support can include reactive groups such as,
for example, carboxy, amino, or hydroxyl groups, which are used for
attachment of the capture reagents. Polymeric solid supports can
include, e.g., polystyrene, polyethylene glycol tetraphthalate,
polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone,
polyacrylonitrile, polymethyl methacrylate,
polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber,
natural rubber, polyethylene, polypropylene,
(poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate,
and polymethylpentene. Suitable solid support particles that can be
used include, e.g., encoded particles, such as Luminex-type encoded
particles, magnetic particles, and glass particles.
Exemplary Uses of Biomarkers
In various exemplary embodiments, methods are provided for
diagnosing lung cancer in an individual by detecting one or more
biomarker values corresponding to one or more biomarkers that are
present in the lung tissue of an individual by any number of
analytical methods, including any of the analytical methods
described herein. These biomarkers are, for example, differentially
expressed in individuals with lung cancer as compared to
individuals without lung cancer, particularly NSCLC. Detection of
the differential expression of a biomarker in an individual can be
used, for example, to permit the early diagnosis of lung cancer, to
distinguish between a benign and malignant pulmonary nodule (such
as, for example, a nodule observed on a computed tomography (CT)
scan), to monitor lung cancer recurrence, or for other clinical
indications, including determination of prognosis and methods of
treatment.
Any of the biomarkers described herein may be used in a variety of
clinical indications for lung cancer, including any of the
following: detection of lung cancer (such as in a high-risk
individual or population); characterizing lung cancer (e.g.,
determining lung cancer type, sub-type, or stage), such as by
distinguishing between non-small cell lung cancer (NSCLC) and small
cell lung cancer (SCLC) and/or between adenocarcinoma and squamous
cell carcinoma (or otherwise facilitating histopathology);
determining whether a lung nodule is a benign nodule or a malignant
lung tumor; determining lung cancer prognosis; monitoring lung
cancer progression or remission; monitoring for lung cancer
recurrence; monitoring metastasis; treatment selection; monitoring
response to a therapeutic agent or other treatment; stratification
of individuals for computed tomography (CT) screening (e.g.,
identifying those individuals at greater risk of lung cancer and
thereby most likely to benefit from spiral-CT screening, thus
increasing the positive predictive value of CT); combining
biomarker testing with additional biomedical information, such as
smoking history, etc., or with nodule size, morphology, etc. (such
as to provide an assay with increased diagnostic performance
compared to CT testing or biomarker testing alone); facilitating
the diagnosis of a pulmonary nodule as malignant or benign;
facilitating clinical decision making once a pulmonary nodule is
observed on CT (e.g., ordering repeat CT scans if the nodule is
deemed to be low risk, such as if a biomarker-based test is
negative, with or without categorization of nodule size, or
considering biopsy if the nodule is deemed medium to high risk,
such as if a biomarker-based test is positive, with or without
categorization of nodule size); and facilitating decisions
regarding clinical follow-up (e.g., whether to implement repeat CT
scans, fine needle biopsy, or thoracotomy after observing a
non-calcified nodule on CT). Biomarker testing may improve positive
predictive value (PPV) over CT screening alone. In addition to
their utilities in conjunction with CT screening, the biomarkers
described herein can also be used in conjunction with any other
imaging modalities used for lung cancer, such as chest X-ray.
Furthermore, the described biomarkers may also be useful in
permitting certain of these uses before indications of lung cancer
are detected by imaging modalities or other clinical correlates, or
before symptoms appear.
As an example of the manner in which any of the biomarkers
described herein can be used to diagnose lung cancer, differential
expression of one or more of the described biomarkers in an
individual who is not known to have lung cancer may indicate that
the individual has lung cancer, thereby enabling detection of lung
cancer at an early stage of the disease when treatment is most
effective, perhaps before the lung cancer is detected by other
means or before symptoms appear. Over-expression of one or more of
the biomarkers during the course of lung cancer may be indicative
of lung cancer progression, e.g., a lung tumor is growing and/or
metastasizing (and thus indicate a poor prognosis), whereas a
decrease in the degree to which one or more of the biomarkers is
differentially expressed (i.e., in subsequent biomarker tests, the
expression level in the individual is moving toward or approaching
a "normal" expression level) may be indicative of lung cancer
remission, e.g., a lung tumor is shrinking (and thus indicate a
good or better prognosis). Similarly, an increase in the degree to
which one or more of the biomarkers is differentially expressed
(i.e., in subsequent biomarker tests, the expression level in the
individual is moving further away from a "normal" expression level)
during the course of lung cancer treatment may indicate that the
lung cancer is progressing and therefore indicate that the
treatment is ineffective, whereas a decrease in differential
expression of one or more of the biomarkers during the course of
lung cancer treatment may be indicative of lung cancer remission
and therefore indicate that the treatment is working successfully.
Additionally, an increase or decrease in the differential
expression of one or more of the biomarkers after an individual has
apparently been cured of lung cancer may be indicative of lung
cancer recurrence. In a situation such as this, for example, the
individual can be re-started on therapy (or the therapeutic regimen
modified such as to increase dosage amount and/or frequency, if the
individual has maintained therapy) at an earlier stage than if the
recurrence of lung cancer was not detected until later.
Furthermore, a differential expression level of one or more of the
biomarkers in an individual may be predictive of the individual's
response to a particular therapeutic agent. In monitoring for lung
cancer recurrence or progression, changes in the biomarker
expression levels may indicate the need for repeat imaging (e.g.,
repeat CT scanning), such as to determine lung cancer activity or
to determine the need for changes in treatment.
Detection of any of the biomarkers described herein may be
particularly useful following, or in conjunction with, lung cancer
treatment, such as to evaluate the success of the treatment or to
monitor lung cancer remission, recurrence, and/or progression
(including metastasis) following treatment. Lung cancer treatment
may include, for example, administration of a therapeutic agent to
the individual, performance of surgery (e.g., surgical resection of
at least a portion of a lung tumor), administration of radiation
therapy, or any other type of lung cancer treatment used in the
art, and any combination of these treatments. For example, any of
the biomarkers may be detected at least once after treatment or may
be detected multiple times after treatment (such as at periodic
intervals), or may be detected both before and after treatment.
Differential expression levels of any of the biomarkers in an
individual over time may be indicative of lung cancer progression,
remission, or recurrence, examples of which include any of the
following: an increase or decrease in the expression level of the
biomarkers after treatment compared with the expression level of
the biomarker before treatment; an increase or decrease in the
expression level of the biomarker at a later time point after
treatment compared with the expression level of the biomarker at an
earlier time point after treatment; and a differential expression
level of the biomarker at a single time point after treatment
compared with normal levels of the biomarker.
As a specific example, the biomarker levels for any of the
biomarkers described herein can be determined in pre-surgery and
post-surgery (e.g., 2-4 weeks after surgery) serum samples. An
increase in the biomarker expression level(s) in the post-surgery
sample compared with the pre-surgery sample can indicate
progression of lung cancer (e.g., unsuccessful surgery), whereas a
decrease in the biomarker expression level(s) in the post-surgery
sample compared with the pre-surgery sample can indicate regression
of lung cancer (e.g., the surgery successfully removed the lung
tumor). Similar analyses of the biomarker levels can be carried out
before and after other forms of treatment, such as before and after
radiation therapy or administration of a therapeutic agent or
cancer vaccine.
In addition to testing biomarker levels as a stand-alone diagnostic
test, biomarker levels can also be done in conjunction with
determination of SNPs or other genetic lesions or variability that
are indicative of increased risk of susceptibility of disease.
(See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).
In addition to testing biomarker levels as a stand-alone diagnostic
test, biomarker levels can also be done in conjunction with CT
screening. For example, the biomarkers may facilitate the medical
and economic justification for implementing CT screening, such as
for screening large asymptomatic populations at risk for lung
cancer (e.g., smokers). For example, a "pre-CT" test of biomarker
levels could be used to stratify high-risk individuals for CT
screening, such as for identifying those who are at highest risk
for lung cancer based on their biomarker levels and who should be
prioritized for CT screening. If a CT test is implemented,
biomarker levels (e.g., as determined by an aptamer assay of serum
or plasma samples) of one or more biomarkers can be measured and
the diagnostic score could be evaluated in conjunction with
additional biomedical information (e.g., tumor parameters
determined by CT testing) to enhance positive predictive value
(PPV) over CT or biomarker testing alone. A "post-CT" aptamer panel
for determining biomarker levels can be used to determine the
likelihood that a pulmonary nodule observed by CT (or other imaging
modality) is malignant or benign.
Detection of any of the biomarkers described herein may be useful
for post-CT testing. For example, biomarker testing may eliminate
or reduce a significant number of false positive tests over CT
alone. Further, biomarker testing may facilitate treatment of
patients. By way of example, if a lung nodule is less than 5 mm in
size, results of biomarker testing may advance patients from "watch
and wait" to biopsy at an earlier time; if a lung nodule is 5-9 mm,
biomarker testing may eliminate the use of a biopsy or thoracotomy
on false positive scans; and if a lung nodule is larger than 10 mm,
biomarker testing may eliminate surgery for a sub-population of
these patients with benign nodules Eliminating the need for biopsy
in some patients based on biomarker testing would be beneficial
because there is significant morbidity associated with nodule
biopsy and difficulty in obtaining nodule tissue depending on the
location of nodule. Similarly, eliminating the need for surgery in
some patients, such as those whose nodules are actually benign,
would avoid unnecessary risks and costs associated with
surgery.
In addition to testing biomarker levels in conjunction with CT
screening (e.g., assessing biomarker levels in conjunction with
size or other characteristics of a lung nodule observed on a CT
scan), information regarding the biomarkers can also be evaluated
in conjunction with other types of data, particularly data that
indicates an individual's risk for lung cancer (e.g., patient
clinical history, symptoms, family history of cancer, risk factors
such as whether or not the individual is a smoker, and/or status of
other biomarkers, etc.). These various data can be assessed by
automated methods, such as a computer program/software, which can
be embodied in a computer or other apparatus/device.
Any of the described biomarkers may also be used in imaging tests.
For example, an imaging agent can be coupled to any of the
described biomarkers, which can be used to aid in lung cancer
diagnosis, to monitor disease progression/remission or metastasis,
to monitor for disease recurrence, or to monitor response to
therapy, among other uses.
Detection and Determination of Biomarkers and Biomarker Values
A biomarker value for the biomarkers described herein can be
detected using any of a variety of known analytical methods. In one
embodiment, a biomarker value is detected using a capture reagent.
As used herein, a "capture agent" or "capture reagent" refers to a
molecule that is capable of binding specifically to a biomarker. In
various embodiments, the capture reagent can be exposed to the
biomarker in solution or can be exposed to the biomarker while the
capture reagent is immobilized on a solid support. In other
embodiments, the capture reagent contains a feature that is
reactive with a secondary feature on a solid support. In these
embodiments, the capture reagent can be exposed to the biomarker in
solution, and then the feature on the capture reagent can be used
in conjunction with the secondary feature on the solid support to
immobilize the biomarker on the solid support. The capture reagent
is selected based on the type of analysis to be conducted. Capture
reagents include but are not limited to aptamers, antibodies,
adnectins, ankyrins, other antibody mimetics and other protein
scaffolds, autoantibodies, chimeras, small molecules, an
F(ab').sub.2 fragment, a single chain antibody fragment, an Fv
fragment, a single chain Fv fragment, a nucleic acid, a lectin, a
ligand-binding receptor, affybodies, nanobodies, imprinted
polymers, avimers, peptidomimetics, a hormone receptor, a cytokine
receptor, and synthetic receptors, and modifications and fragments
of these.
In some embodiments, a biomarker value is detected using a
biomarker/capture reagent complex.
In other embodiments, the biomarker value is derived from the
biomarker/capture reagent complex and is detected indirectly, such
as, for example, as a result of a reaction that is subsequent to
the biomarker/capture reagent interaction, but is dependent on the
formation of the biomarker/capture reagent complex.
In some embodiments, the biomarker value is detected directly from
the biomarker in a biological sample.
In one embodiment, the biomarkers are detected using a multiplexed
format that allows for the simultaneous detection of two or more
biomarkers in a biological sample. In one embodiment of the
multiplexed format, capture reagents are immobilized, directly or
indirectly, covalently or non-covalently, in discrete locations on
a solid support. In another embodiment, a multiplexed format uses
discrete solid supports where each solid support has a unique
capture reagent associated with that solid support, such as, for
example quantum dots. In another embodiment, an individual device
is used for the detection of each one of multiple biomarkers to be
detected in a biological sample. Individual devices can be
configured to permit each biomarker in the biological sample to be
processed simultaneously. For example, a microtiter plate can be
used such that each well in the plate is used to uniquely analyze
one of multiple biomarkers to be detected in a biological
sample.
In one or more of the foregoing embodiments, a fluorescent tag can
be used to label a component of the biomarker/capture complex to
enable the detection of the biomarker value. In various
embodiments, the fluorescent label can be conjugated to a capture
reagent specific to any of the biomarkers described herein using
known techniques, and the fluorescent label can then be used to
detect the corresponding biomarker value. Suitable fluorescent
labels include rare earth chelates, fluorescein and its
derivatives, rhodamine and its derivatives, dansyl,
allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas
Red, and other such compounds.
In one embodiment, the fluorescent label is a fluorescent dye
molecule. In some embodiments, the fluorescent dye molecule
includes at least one substituted indolium ring system in which the
substituent on the 3-carbon of the indolium ring contains a
chemically reactive group or a conjugated substance. In some
embodiments, the dye molecule includes an AlexFluor molecule, such
as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647,
AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye
molecule includes a first type and a second type of dye molecule,
such as, e.g., two different AlexaFluor molecules. In other
embodiments, the dye molecule includes a first type and a second
type of dye molecule, and the two dye molecules have different
emission spectra.
Fluorescence can be measured with a variety of instrumentation
compatible with a wide range of assay formats. For example,
spectrofluorimeters have been designed to analyze microtiter
plates, microscope slides, printed arrays, cuvettes, etc. See
Principles of Fluorescence Spectroscopy, by J. R. Lakowicz,
Springer Science+Business Media, Inc., 2004; Bioluminescence &
Chemiluminescence: Progress & Current Applications; Philip E.
Stanley and Larry J. Kricka editors, World Scientific Publishing
Company, January 2002.
In one or more of the foregoing embodiments, a chemiluminescence
tag can optionally be used to label a component of the
biomarker/capture complex to enable the detection of a biomarker
value. Suitable chemiluminescent materials include any of oxalyl
chloride, Rodamin 6G, Ru(bipy).sub.3.sup.2+, TMAE
(tetrakis(dimethylamino)ethylene), pyrogallol
(1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, aryl
oxalates, acridinium esters, dioxetanes, and others.
In yet other embodiments, the detection method includes an
enzyme/substrate combination that generates a detectable signal
that corresponds to the biomarker value. Generally, the enzyme
catalyzes a chemical alteration of the chromogenic substrate which
can be measured using various techniques, including
spectrophotometry, fluorescence, and chemiluminescence. Suitable
enzymes include, for example, luciferases, luciferin, malate
dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline
phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose
oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase,
uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and
the like.
In yet other embodiments, the detection method can be a combination
of fluorescence, chemiluminescence, radionuclide or
enzyme/substrate combinations that generate a measurable signal.
Multimodal signaling could have unique and advantageous
characteristics in biomarker assay formats.
More specifically, the biomarker values for the biomarkers
described herein can be detected using known analytical methods
including, singleplex aptamer assays, multiplexed aptamer assays,
singleplex or multiplexed immunoassays, mRNA expression profiling,
miRNA expression profiling, mass spectrometric analysis,
histological/cytological methods, etc. as detailed below.
Determination of Biomarker Values Using Aptamer-Based Assays
Assays directed to the detection and quantification of
physiologically significant molecules in biological samples and
other samples are important tools in scientific research and in the
health care field. One class of such assays involves the use of a
microarray that includes one or more aptamers immobilized on a
solid support. The aptamers are each capable of binding to a target
molecule in a highly specific manner and with very high affinity.
See, e.g., U.S. Pat. No. 5,475,096 entitled "Nucleic Acid Ligands,"
see also, e.g., U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715,
each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip".
Once the microarray is contacted with a sample, the aptamers bind
to their respective target molecules present in the sample and
thereby enable a determination of a biomarker value corresponding
to a biomarker.
As used herein, an "aptamer" refers to a nucleic acid that has a
specific binding affinity for a target molecule. It is recognized
that affinity interactions are a matter of degree; however, in this
context, the "specific binding affinity" of an aptamer for its
target means that the aptamer binds to its target generally with a
much higher degree of affinity than it binds to other components in
a test sample. An "aptamer" is a set of copies of one type or
species of nucleic acid molecule that has a particular nucleotide
sequence. An aptamer can include any suitable number of
nucleotides, including any number of chemically modified
nucleotides. "Aptamers" refers to more than one such set of
molecules. Different aptamers can have either the same or different
numbers of nucleotides. Aptamers can be DNA or RNA or chemically
modified nucleic acids and can be single stranded, double stranded,
or contain double stranded regions, and can include higher ordered
structures. An aptamer can also be a photoaptamer, where a
photoreactive or chemically reactive functional group is included
in the aptamer to allow it to be covalently linked to its
corresponding target. Any of the aptamer methods disclosed herein
can include the use of two or more aptamers that specifically bind
the same target molecule. As further described below, an aptamer
may include a tag. If an aptamer includes a tag, all copies of the
aptamer need not have the same tag. Moreover, if different aptamers
each include a tag, these different aptamers can have either the
same tag or a different tag.
An aptamer can be identified using any known method, including the
SELEX process. Once identified, an aptamer can be prepared or
synthesized in accordance with any known method, including chemical
synthetic methods and enzymatic synthetic methods.
The terms "SELEX" and "SELEX process" are used interchangeably
herein to refer generally to a combination of (1) the selection of
aptamers that interact with a target molecule in a desirable
manner, for example binding with high affinity to a protein, with
(2) the amplification of those selected nucleic acids. The SELEX
process can be used to identify aptamers with high affinity to a
specific target or biomarker.
SELEX generally includes preparing a candidate mixture of nucleic
acids, binding of the candidate mixture to the desired target
molecule to form an affinity complex, separating the affinity
complexes from the unbound candidate nucleic acids, separating and
isolating the nucleic acid from the affinity complex, purifying the
nucleic acid, and identifying a specific aptamer sequence. The
process may include multiple rounds to further refine the affinity
of the selected aptamer. The process can include amplification
steps at one or more points in the process. See, e.g., U.S. Pat.
No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process
can be used to generate an aptamer that covalently binds its target
as well as an aptamer that non-covalently binds its target. See,
e.g., U.S. Pat. No. 5,705,337 entitled "Systematic Evolution of
Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."
The SELEX process can be used to identify high-affinity aptamers
containing modified nucleotides that confer improved
characteristics on the aptamer, such as, for example, improved in
vivo stability or improved delivery characteristics. Examples of
such modifications include chemical substitutions at the ribose
and/or phosphate and/or base positions. SELEX process-identified
aptamers containing modified nucleotides are described in U.S. Pat.
No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands
Containing Modified Nucleotides," which describes oligonucleotides
containing nucleotide derivatives chemically modified at the 5'-
and 2'-positions of pyrimidines. U.S. Pat. No. 5,580,737, see
supra, describes highly specific aptamers containing one or more
nucleotides modified with 2'-amino (2'-NH.sub.2), 2'-fluoro (2'-F),
and/or 2'-O-methyl (2'-OMe). See also, U.S. Patent Application
Publication 20090098549, entitled "SELEX and PHOTOSELEX," which
describes nucleic acid libraries having expanded physical and
chemical properties and their use in SELEX and photoSELEX.
SELEX can also be used to identify aptamers that have desirable
off-rate characteristics. See U.S. Patent Application Publication
20090004667, entitled "Method for Generating Aptamers with Improved
Off-Rates," which describes improved SELEX methods for generating
aptamers that can bind to target molecules. Methods for producing
aptamers and photoaptamers having slower rates of dissociation from
their respective target molecules are described. The methods
involve contacting the candidate mixture with the target molecule,
allowing the formation of nucleic acid-target complexes to occur,
and performing a slow off-rate enrichment process wherein nucleic
acid-target complexes with fast dissociation rates will dissociate
and not reform, while complexes with slow dissociation rates will
remain intact. Additionally, the methods include the use of
modified nucleotides in the production of candidate nucleic acid
mixtures to generate aptamers with improved off-rate
performance.
A variation of this assay employs aptamers that include
photoreactive functional groups that enable the aptamers to
covalently bind or "photocrosslink" their target molecules. See,
e.g., U.S. Pat. No. 6,544,776 entitled "Nucleic Acid Ligand
Diagnostic Biochip." These photoreactive aptamers are also referred
to as photoaptamers. See, e.g., U.S. Pat. Nos. 5,763,177,
6,001,577, and 6,291,184, each of which is entitled "Systematic
Evolution of Nucleic Acid Ligands by Exponential Enrichment:
Photoselection of Nucleic Acid Ligands and Solution SELEX," see
also, e.g., U.S. Pat. No. 6,458,539, entitled "Photoselection of
Nucleic Acid Ligands." After the microarray is contacted with the
sample and the photoaptamers have had an opportunity to bind to
their target molecules, the photoaptamers are photoactivated, and
the solid support is washed to remove any non-specifically bound
molecules. Harsh wash conditions may be used, since target
molecules that are bound to the photoaptamers are generally not
removed, due to the covalent bonds created by the photoactivated
functional group(s) on the photoaptamers. In this manner, the assay
enables the detection of a biomarker value corresponding to a
biomarker in the test sample.
In both of these assay formats, the aptamers are immobilized on the
solid support prior to being contacted with the sample. Under
certain circumstances, however, immobilization of the aptamers
prior to contact with the sample may not provide an optimal assay.
For example, pre-immobilization of the aptamers may result in
inefficient mixing of the aptamers with the target molecules on the
surface of the solid support, perhaps leading to lengthy reaction
times and, therefore, extended incubation periods to permit
efficient binding of the aptamers to their target molecules.
Further, when photoaptamers are employed in the assay and depending
upon the material utilized as a solid support, the solid support
may tend to scatter or absorb the light used to effect the
formation of covalent bonds between the photoaptamers and their
target molecules. Moreover, depending upon the method employed,
detection of target molecules bound to their aptamers can be
subject to imprecision, since the surface of the solid support may
also be exposed to and affected by any labeling agents that are
used. Finally, immobilization of the aptamers on the solid support
generally involves an aptamer-preparation step (i.e., the
immobilization) prior to exposure of the aptamers to the sample,
and this preparation step may affect the activity or functionality
of the aptamers.
Aptamer assays that permit an aptamer to capture its target in
solution and then employ separation steps that are designed to
remove specific components of the aptamer-target mixture prior to
detection have also been described (see U.S. Patent Application
Publication 20090042206, entitled "Multiplexed Analyses of Test
Samples"). The described aptamer assay methods enable the detection
and quantification of a non-nucleic acid target (e.g., a protein
target) in a test sample by detecting and quantifying a nucleic
acid (i.e., an aptamer). The described methods create a nucleic
acid surrogate (i.e, the aptamer) for detecting and quantifying a
non-nucleic acid target, thus allowing the wide variety of nucleic
acid technologies, including amplification, to be applied to a
broader range of desired targets, including protein targets.
Aptamers can be constructed to facilitate the separation of the
assay components from an aptamer biomarker complex (or photoaptamer
biomarker covalent complex) and permit isolation of the aptamer for
detection and/or quantification. In one embodiment, these
constructs can include a cleavable or releasable element within the
aptamer sequence. In other embodiments, additional functionality
can be introduced into the aptamer, for example, a labeled or
detectable component, a spacer component, or a specific binding tag
or immobilization element. For example, the aptamer can include a
tag connected to the aptamer via a cleavable moiety, a label, a
spacer component separating the label, and the cleavable moiety. In
one embodiment, a cleavable element is a photocleavable linker. The
photocleavable linker can be attached to a biotin moiety and a
spacer section, can include an NHS group for derivatization of
amines, and can be used to introduce a biotin group to an aptamer,
thereby allowing for the release of the aptamer later in an assay
method.
Homogenous assays, done with all assay components in solution, do
not require separation of sample and reagents prior to the
detection of signal. These methods are rapid and easy to use. These
methods generate signal based on a molecular capture or binding
reagent that reacts with its specific target. For lung cancer, the
molecular capture reagents would be an aptamer or an antibody or
the like and the specific target would be a lung cancer biomarker
of Table 20.
In one embodiment, a method for signal generation takes advantage
of anisotropy signal change due to the interaction of a
fluorophore-labeled capture reagent with its specific biomarker
target. When the labeled capture reacts with its target, the
increased molecular weight causes the rotational motion of the
fluorophore attached to the complex to become much slower changing
the anisotropy value. By monitoring the anisotropy change, binding
events may be used to quantitatively measure the biomarkers in
solutions. Other methods include fluorescence polarization assays,
molecular beacon methods, time resolved fluorescence quenching,
chemiluminescence, fluorescence resonance energy transfer, and the
like.
An exemplary solution-based aptamer assay that can be used to
detect a biomarker value corresponding to a biomarker in a
biological sample includes the following: (a) preparing a mixture
by contacting the biological sample with an aptamer that includes a
first tag and has a specific affinity for the biomarker, wherein an
aptamer affinity complex is formed when the biomarker is present in
the sample; (b) exposing the mixture to a first solid support
including a first capture element, and allowing the first tag to
associate with the first capture element; (c) removing any
components of the mixture not associated with the first solid
support; (d) attaching a second tag to the biomarker component of
the aptamer affinity complex; (e) releasing the aptamer affinity
complex from the first solid support; (f) exposing the released
aptamer affinity complex to a second solid support that includes a
second capture element and allowing the second tag to associate
with the second capture element; (g) removing any non-complexed
aptamer from the mixture by partitioning the non-complexed aptamer
from the aptamer affinity complex; (h) eluting the aptamer from the
solid support; and (i) detecting the biomarker by detecting the
aptamer component of the aptamer affinity complex.
Determination of Biomarker Values using Immunoassays
Immunoassay methods are based on the reaction of an antibody to its
corresponding target or analyte and can detect the analyte in a
sample depending on the specific assay format. To improve
specificity and sensitivity of an assay method based on
immuno-reactivity, monoclonal antibodies are often used because of
their specific epitope recognition. Polyclonal antibodies have also
been successfully used in various immunoassays because of their
increased affinity for the target as compared to monoclonal
antibodies Immunoassays have been designed for use with a wide
range of biological sample matrices Immunoassay formats have been
designed to provide qualitative, semi-quantitative, and
quantitative results.
Quantitative results are generated through the use of a standard
curve created with known concentrations of the specific analyte to
be detected. The response or signal from an unknown sample is
plotted onto the standard curve, and a quantity or value
corresponding to the target in the unknown sample is
established.
Numerous immunoassay formats have been designed. ELISA or EIA can
be quantitative for the detection of an analyte. This method relies
on attachment of a label to either the analyte or the antibody and
the label component includes, either directly or indirectly, an
enzyme. ELISA tests may be formatted for direct, indirect,
competitive, or sandwich detection of the analyte. Other methods
rely on labels such as, for example, radioisotopes (I.sup.125) or
fluorescence. Additional techniques include, for example,
agglutination, nephelometry, turbidimetry, Western blot,
immunoprecipitation, immunocytochemistry, immunohistochemistry,
flow cytometry, Luminex assay, and others (see ImmunoAssay: A
Practical Guide, edited by Brian Law, published by Taylor &
Francis, Ltd., 2005 edition).
Exemplary assay formats include enzyme-linked immunosorbent assay
(ELISA), radioimmunoassay, fluorescent, chemiluminescence, and
fluorescence resonance energy transfer (FRET) or time resolved-FRET
(TR-FRET) immunoassays. Examples of procedures for detecting
biomarkers include biomarker immunoprecipitation followed by
quantitative methods that allow size and peptide level
discrimination, such as gel electrophoresis, capillary
electrophoresis, planar electrochromatography, and the like.
Methods of detecting and/or quantifying a detectable label or
signal generating material depend on the nature of the label. The
products of reactions catalyzed by appropriate enzymes (where the
detectable label is an enzyme; see above) can be, without
limitation, fluorescent, luminescent, or radioactive or they may
absorb visible or ultraviolet light. Examples of detectors suitable
for detecting such detectable labels include, without limitation,
x-ray film, radioactivity counters, scintillation counters,
spectrophotometers, colorimeters, fluorometers, luminometers, and
densitometers.
Any of the methods for detection can be performed in any format
that allows for any suitable preparation, processing, and analysis
of the reactions. This can be, for example, in multi-well assay
plates (e.g., 96 wells or 384 wells) or using any suitable array or
microarray. Stock solutions for various agents can be made manually
or robotically, and all subsequent pipetting, diluting, mixing,
distribution, washing, incubating, sample readout, data collection
and analysis can be done robotically using commercially available
analysis software, robotics, and detection instrumentation capable
of detecting a detectable label.
Determination of Biomarker Values Using Gene Expression
Profiling
Measuring mRNA in a biological sample may be used as a surrogate
for detection of the level of the corresponding protein in the
biological sample. Thus, any of the biomarkers or biomarker panels
described herein can also be detected by detecting the appropriate
RNA.
mRNA expression levels are measured by reverse transcription
quantitative polymerase chain reaction (RT-PCR followed with qPCR).
RT-PCR is used to create a cDNA from the mRNA. The cDNA may be used
in a qPCR assay to produce fluorescence as the DNA amplification
process progresses. By comparison to a standard curve, qPCR can
produce an absolute measurement such as number of copies of mRNA
per cell. Northern blots, microarrays, Invader assays, and RT-PCR
combined with capillary electrophoresis have all been used to
measure expression levels of mRNA in a sample (see Gene Expression
Profiling: Methods and Protocols, Richard A. Shimkets, editor,
Humana Press, 2004).
miRNA molecules are small RNAs that are non-coding but may regulate
gene expression. Any of the methods suited to the measurement of
mRNA expression levels can also be used for the corresponding
miRNA. Recently many laboratories have investigated the use of
miRNAs as biomarkers for disease. Many diseases involve wide-spread
transcriptional regulation, and it is not surprising that miRNAs
might find a role as biomarkers. The connection between miRNA
concentrations and disease is often even less clear than the
connections between protein levels and disease, yet the value of
miRNA biomarkers might be substantial. Of course, as with any RNA
expressed differentially during disease, the problems facing the
development of an in vitro diagnostic product will include the
requirement that the miRNAs survive in the diseased cell and are
easily extracted for analysis, or that the miRNAs are released into
blood or other matrices where they must survive long enough to be
measured. Protein biomarkers have similar requirements, although
many potential protein biomarkers are secreted intentionally at the
site of pathology and function, during disease, in a paracrine
fashion. Many potential protein biomarkers are designed to function
outside the cells within which those proteins are synthesized.
Detection of Biomarkers Using In Vivo Molecular Imaging
Technologies
Any of the described biomarkers (see Table 20) may also be used in
molecular imaging tests. For example, an imaging agent can be
coupled to any of the described biomarkers, which can be used to
aid in lung cancer diagnosis, to monitor disease
progression/remission or metastasis, to monitor for disease
recurrence, or to monitor response to therapy, among other
uses.
In vivo imaging technologies provide non-invasive methods for
determining the state of a particular disease in the body of an
individual. For example, entire portions of the body, or even the
entire body, may be viewed as a three dimensional image, thereby
providing valuable information concerning morphology and structures
in the body. Such technologies may be combined with the detection
of the biomarkers described herein to provide information
concerning the cancer status, in particular the lung cancer status,
of an individual.
The use of in vivo molecular imaging technologies is expanding due
to various advances in technology. These advances include the
development of new contrast agents or labels, such as radiolabels
and/or fluorescent labels, which can provide strong signals within
the body; and the development of powerful new imaging technology,
which can detect and analyze these signals from outside the body,
with sufficient sensitivity and accuracy to provide useful
information. The contrast agent can be visualized in an appropriate
imaging system, thereby providing an image of the portion or
portions of the body in which the contrast agent is located. The
contrast agent may be bound to or associated with a capture
reagent, such as an aptamer or an antibody, for example, and/or
with a peptide or protein, or an oligonucleotide (for example, for
the detection of gene expression), or a complex containing any of
these with one or more macromolecules and/or other particulate
forms.
The contrast agent may also feature a radioactive atom that is
useful in imaging. Suitable radioactive atoms include
technetium-99m or iodine-123 for scintigraphic studies. Other
readily detectable moieties include, for example, spin labels for
magnetic resonance imaging (MRI) such as, for example, iodine-123
again, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15,
oxygen-17, gadolinium, manganese or iron. Such labels are well
known in the art and could easily be selected by one of ordinary
skill in the art.
Standard imaging techniques include but are not limited to magnetic
resonance imaging, computed tomography scanning, positron emission
tomography (PET), single photon emission computed tomography
(SPECT), and the like. For diagnostic in vivo imaging, the type of
detection instrument available is a major factor in selecting a
given contrast agent, such as a given radionuclide and the
particular biomarker that it is used to target (protein, mRNA, and
the like). The radionuclide chosen typically has a type of decay
that is detectable by a given type of instrument. Also, when
selecting a radionuclide for in vivo diagnosis, its half-life
should be long enough to enable detection at the time of maximum
uptake by the target tissue but short enough that deleterious
radiation of the host is minimized.
Exemplary imaging techniques include but are not limited to PET and
SPECT, which are imaging techniques in which a radionuclide is
synthetically or locally administered to an individual. The
subsequent uptake of the radiotracer is measured over time and used
to obtain information about the targeted tissue and the biomarker.
Because of the high-energy (gamma-ray) emissions of the specific
isotopes employed and the sensitivity and sophistication of the
instruments used to detect them, the two-dimensional distribution
of radioactivity may be inferred from outside of the body.
Commonly used positron-emitting nuclides in PET include, for
example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18.
Isotopes that decay by electron capture and/or gamma-emission are
used in SPECT and include, for example iodine-123 and
technetium-99m. An exemplary method for labeling amino acids with
technetium-99m is the reduction of pertechnetate ion in the
presence of a chelating precursor to form the labile
technetium-99m-precursor complex, which, in turn, reacts with the
metal binding group of a bifunctionally modified chemotactic
peptide to form a technetium-99m-chemotactic peptide conjugate.
Antibodies are frequently used for such in vivo imaging diagnostic
methods. The preparation and use of antibodies for in vivo
diagnosis is well known in the art. Labeled antibodies which
specifically bind any of the biomarkers in Table 20 can be injected
into an individual suspected of having a certain type of cancer
(e.g., lung cancer), detectable according to the particular
biomarker used, for the purpose of diagnosing or evaluating the
disease status of the individual. The label used will be selected
in accordance with the imaging modality to be used, as previously
described. Localization of the label permits determination of the
spread of the cancer. The amount of label within an organ or tissue
also allows determination of the presence or absence of cancer in
that organ or tissue.
Similarly, aptamers may be used for such in vivo imaging diagnostic
methods. For example, an aptamer that was used to identify a
particular biomarker described in Table 20 (and therefore binds
specifically to that particular biomarker) may be appropriately
labeled and injected into an individual suspected of having lung
cancer, detectable according to the particular biomarker, for the
purpose of diagnosing or evaluating the lung cancer status of the
individual. The label used will be selected in accordance with the
imaging modality to be used, as previously described. Localization
of the label permits determination of the spread of the cancer. The
amount of label within an organ or tissue also allows determination
of the presence or absence of cancer in that organ or tissue.
Aptamer-directed imaging agents could have unique and advantageous
characteristics relating to tissue penetration, tissue
distribution, kinetics, elimination, potency, and selectivity as
compared to other imaging agents.
Such techniques may also optionally be performed with labeled
oligonucleotides, for example, for detection of gene expression
through imaging with antisense oligonucleotides. These methods are
used for in situ hybridization, for example, with fluorescent
molecules or radionuclides as the label. Other methods for
detection of gene expression include, for example, detection of the
activity of a reporter gene.
Another general type of imaging technology is optical imaging, in
which fluorescent signals within the subject are detected by an
optical device that is external to the subject. These signals may
be due to actual fluorescence and/or to bioluminescence.
Improvements in the sensitivity of optical detection devices have
increased the usefulness of optical imaging for in vivo diagnostic
assays.
The use of in vivo molecular biomarker imaging is increasing,
including for clinical trials, for example, to more rapidly measure
clinical efficacy in trials for new cancer therapies and/or to
avoid prolonged treatment with a placebo for those diseases, such
as multiple sclerosis, in which such prolonged treatment may be
considered to be ethically questionable.
For a review of other techniques, see N. Blow, Nature Methods, 6,
465-469, 2009.
Determination of Biomarker Values Using Histology/Cytology
Methods
For evaluation of lung cancer, a variety of tissue samples may be
used in histological or cytological methods. Sample selection
depends on the primary tumor location and sites of metastases. For
example, endo- and trans-bronchial biopsies, fine needle aspirates,
cutting needles, and core biopsies can be used for histology.
Bronchial washing and brushing, pleural aspiration, and sputum, can
be used for cyotology. While cytological analysis is still used in
the diagnosis of lung cancer, histological methods are known to
provide better sensitivity for the detection of cancer. Any of the
biomarkers identified herein that were shown to be up-regulated
(see Table 19) in the individuals with lung cancer can be used to
stain a histological specimen as an indication of disease.
In one embodiment, one or more capture reagent(s) specific to the
corresponding biomarker(s) are used in a cytological evaluation of
a lung cell sample and may include one or more of the following:
collecting a cell sample, fixing the cell sample, dehydrating,
clearing, immobilizing the cell sample on a microscope slide,
permeabilizing the cell sample, treating for analyte retrieval,
staining, destaining, washing, blocking, and reacting with one or
more capture reagent/s in a buffered solution. In another
embodiment, the cell sample is produced from a cell block.
In another embodiment, one or more capture reagent/s specific to
the corresponding biomarkers are used in a histological evaluation
of a lung tissue sample and may include one or more of the
following: collecting a tissue specimen, fixing the tissue sample,
dehydrating, clearing, immobilizing the tissue sample on a
microscope slide, permeabilizing the tissue sample, treating for
analyte retrieval, staining, destaining, washing, blocking,
rehydrating, and reacting with capture reagent/s in a buffered
solution. In another embodiment, fixing and dehydrating are
replaced with freezing.
In another embodiment, the one or more aptamer/s specific to the
corresponding biomarker/s are reacted with the histological or
cytological sample and can serve as the nucleic acid target in a
nucleic acid amplification method. Suitable nucleic acid
amplification methods include, for example, PCR, q-beta replicase,
rolling circle amplification, strand displacement, helicase
dependent amplification, loop mediated isothermal amplification,
ligase chain reaction, and restriction and circularization aided
rolling circle amplification.
In one embodiment, the one or more capture reagent(s) specific to
the corresponding biomarkers for use in the histological or
cytological evaluation are mixed in a buffered solution that can
include any of the following: blocking materials, competitors,
detergents, stabilizers, carrier nucleic acid, polyanionic
materials, etc.
A "cytology protocol" generally includes sample collection, sample
fixation, sample immobilization, and staining. "Cell preparation"
can include several processing steps after sample collection,
including the use of one or more slow off-rate aptamers for the
staining of the prepared cells.
Sample collection can include directly placing the sample in an
untreated transport container, placing the sample in a transport
container containing some type of media, or placing the sample
directly onto a slide (immobilization) without any treatment or
fixation.
Sample immobilization can be improved by applying a portion of the
collected specimen to a glass slide that is treated with
polylysine, gelatin, or a silane. Slides can be prepared by
smearing a thin and even layer of cells across the slide. Care is
generally taken to minimize mechanical distortion and drying
artifacts. Liquid specimens can be processed in a cell block
method. Or, alternatively, liquid specimens can be mixed 1:1 with
the fixative solution for about 10 minutes at room temperature.
Cell blocks can be prepared from residual effusions, sputum, urine
sediments, gastrointestinal fluids, cell scraping, or fine needle
aspirates. Cells are concentrated or packed by centrifugation or
membrane filtration. A number of methods for cell block preparation
have been developed. Representative procedures include the fixed
sediment, bacterial agar, or membrane filtration methods. In the
fixed sediment method, the cell sediment is mixed with a fixative
like Bouins, picric acid, or buffered formalin and then the mixture
is centrifuged to pellet the fixed cells. The supernatant is
removed, drying the cell pellet as completely as possible. The
pellet is collected and wrapped in lens paper and then placed in a
tissue cassette. The tissue cassette is placed in a jar with
additional fixative and processed as a tissue sample. Agar method
is very similar but the pellet is removed and dried on paper towel
and then cut in half. The cut side is placed in a drop of melted
agar on a glass slide and then the pellet is covered with agar
making sure that no bubbles form in the agar. The agar is allowed
to harden and then any excess agar is trimmed away. This is placed
in a tissue cassette and the tissue process completed.
Alternatively, the pellet may be directly suspended in 2% liquid
agar at 65.degree. C. and the sample centrifuged. The agar cell
pellet is allowed to solidify for an hour at 4.degree. C. The solid
agar may be removed from the centrifuge tube and sliced in half.
The agar is wrapped in filter paper and then the tissue cassette.
Processing from this point forward is as described above.
Centrifugation can be replaced in any these procedures with
membrane filtration. Any of these processes may be used to generate
a "cell block sample".
Cell blocks can be prepared using specialized resin including
Lowicryl resins, LR White, LR Gold, Unicryl, and MonoStep. These
resins have low viscosity and can be polymerized at low
temperatures and with ultra violet (UV) light. The embedding
process relies on progressively cooling the sample during
dehydration, transferring the sample to the resin, and polymerizing
a block at the final low temperature at the appropriate UV
wavelength.
Cell block sections can be stained with hematoxylin-eosin for
cytomorphological examination while additional sections are used
for examination for specific markers.
Whether the process is cytologoical or histological, the sample may
be fixed prior to additional processing to prevent sample
degradation. This process is called "fixation" and describes a wide
range of materials and procedures that may be used interchangeably.
The sample fixation protocol and reagents are best selected
empirically based on the targets to be detected and the specific
cell/tissue type to be analyzed. Sample fixation relies on reagents
such as ethanol, polyethylene glycol, methanol, formalin, or
isopropanol. The samples should be fixed as soon after collection
and affixation to the slide as possible. However, the fixative
selected can introduce structural changes into various molecular
targets making their subsequent detection more difficult. The
fixation and immobilization processes and their sequence can modify
the appearance of the cell and these changes must be anticipated
and recognized by the cytotechnologist. Fixatives can cause
shrinkage of certain cell types and cause the cytoplasm to appear
granular or reticular. Many fixatives function by crosslinking
cellular components. This can damage or modify specific epitopes,
generate new epitopes, cause molecular associations, and reduce
membrane permeability. Formalin fixation is one of the most common
cytological/histological approaches. Formalin forms methyl bridges
between neighboring proteins or within proteins. Precipitation or
coagulation is also used for fixation and ethanol is frequently
used in this type of fixation. A combination of crosslinking and
precipitation can also be used for fixation. A strong fixation
process is best at preserving morphological information while a
weaker fixation process is best for the preservation of molecular
targets.
A representative fixative is 50% absolute ethanol, 2 mM
polyethylene glycol (PEG), 1.85% formaldehyde. Variations on this
formulation include ethanol (50% to 95%), methanol (20%-50%), and
formalin (formaldehyde) only. Another common fixative is 2% PEG
1500, 50% ethanol, and 3% methanol. Slides are place in the
fixative for about 10 to 15 minutes at room temperature and then
removed and allowed to dry. Once slides are fixed they can be
rinsed with a buffered solution like PBS.
A wide range of dyes can be used to differentially highlight and
contrast or "stain" cellular, sub-cellular, and tissue features or
morphological structures. Hematoylin is used to stain nuclei a blue
or black color. Orange G-6 and Eosin Azure both stain the cell's
cytoplasm. Orange G stains keratin and glycogen containing cells
yellow. Eosin Y is used to stain nucleoli, cilia, red blood cells,
and superficial epithelial squamous cells. Romanowsky stains are
used for air dried slides and are useful in enhancing pleomorphism
and distinguishing extracellular from intracytoplasmic
material.
The staining process can include a treatment to increase the
permeability of the cells to the stain. Treatment of the cells with
a detergent can be used to increase permeability. To increase cell
and tissue permeability, fixed samples can be further treated with
solvents, saponins, or non-ionic detergents. Enzymatic digestion
can also improve the accessibility of specific targets in a tissue
sample.
After staining, the sample is dehydrated using a succession of
alcohol rinses with increasing alcohol concentration. The final
wash is done with xylene or a xylene substitute, such as a citrus
terpene, that has a refractive index close to that of the coverslip
to be applied to the slide. This final step is referred to as
clearing. Once the sample is dehydrated and cleared, a mounting
medium is applied. The mounting medium is selected to have a
refractive index close to the glass and is capable of bonding the
coverslip to the slide. It will also inhibit the additional drying,
shrinking, or fading of the cell sample.
Regardless of the stains or processing used, the final evaluation
of the lung cytological specimen is made by some type of microscopy
to permit a visual inspection of the morphology and a determination
of the marker's presence or absence. Exemplary microscopic methods
include brightfield, phase contrast, fluorescence, and differential
interference contrast.
If secondary tests are required on the sample after examination,
the coverslip may be removed and the slide destained. Destaining
involves using the original solvent systems used in staining the
slide originally without the added dye and in a reverse order to
the original staining procedure. Destaining may also be completed
by soaking the slide in an acid alcohol until the cells are
colorless. Once colorless the slides are rinsed well in a water
bath and the second staining procedure applied.
In addition, specific molecular differentiation may be possible in
conjunction with the cellular morphological analysis through the
use of specific molecular reagents such as antibodies or nucleic
acid probes or aptamers. This improves the accuracy of diagnostic
cytology. Micro-dissection can be used to isolate a subset of cells
for additional evaluation, in particular, for genetic evaluation of
abnormal chromosomes, gene expression, or mutations.
Preparation of a tissue sample for histological evaluation involves
fixation, dehydration, infiltration, embedding, and sectioning. The
fixation reagents used in histology are very similar or identical
to those used in cytology and have the same issues of preserving
morphological features at the expense of molecular ones such as
individual proteins. Time can be saved if the tissue sample is not
fixed and dehydrated but instead is frozen and then sectioned while
frozen. This is a more gentle processing procedure and can preserve
more individual markers. However, freezing is not acceptable for
long term storage of a tissue sample as subcellular information is
lost due to the introduction of ice crystals. Ice in the frozen
tissue sample also prevents the sectioning process from producing a
very thin slice and thus some microscopic resolution and imaging of
subcellular structures can be lost. In addition to formalin
fixation, osmium tetroxide is used to fix and stain phospholipids
(membranes).
Dehydration of tissues is accomplished with successive washes of
increasing alcohol concentration. Clearing employs a material that
is miscible with alcohol and the embedding material and involves a
stepwise process starting at 50:50 alcohol:clearing reagent and
then 100% clearing agent (xylene or xylene substitute).
Infiltration involves incubating the tissue with a liquid form of
the embedding agent (warm wax, nitrocellulose solution) first at
50:50 embedding agent: clearing agent and the 100% embedding agent.
Embedding is completed by placing the tissue in a mold or cassette
and filling with melted embedding agent such as wax, agar, or
gelatin. The embedding agent is allowed to harden. The hardened
tissue sample may then be sliced into thin section for staining and
subsequent examination.
Prior to staining, the tissue section is dewaxed and rehydrated.
Xylene is used to dewax the section, one or more changes of xylene
may be used, and the tissue is rehydrated by successive washes in
alcohol of decreasing concentration. Prior to dewax, the tissue
section may be heat immobilized to a glass slide at about
80.degree. C. for about 20 minutes.
Laser capture micro-dissection allows the isolation of a subset of
cells for further analysis from a tissue section.
As in cytology, to enhance the visualization of the microscopic
features, the tissue section or slice can be stained with a variety
of stains. A large menu of commercially available stains can be
used to enhance or identify specific features.
To further increase the interaction of molecular reagents with
cytological/histological samples, a number of techniques for
"analyte retrieval" have been developed. The first such technique
uses high temperature heating of a fixed sample. This method is
also referred to as heat-induced epitope retrieval or HIER. A
variety of heating techniques have been used, including steam
heating, microwaving, autoclaving, water baths, and pressure
cooking or a combination of these methods of heating. Analyte
retrieval solutions include, for example, water, citrate, and
normal saline buffers. The key to analyte retrieval is the time at
high temperature but lower temperatures for longer times have also
been successfully used. Another key to analyte retrieval is the pH
of the heating solution. Low pH has been found to provide the best
immunostaining but also gives rise to backgrounds that frequently
require the use of a second tissue section as a negative control.
The most consistent benefit (increased immunostaining without
increase in background) is generally obtained with a high pH
solution regardless of the buffer composition. The analyte
retrieval process for a specific target is empirically optimized
for the target using heat, time, pH, and buffer composition as
variables for process optimization. Using the microwave analyte
retrieval method allows for sequential staining of different
targets with antibody reagents. But the time required to achieve
antibody and enzyme complexes between staining steps has also been
shown to degrade cell membrane analytes. Microwave heating methods
have improved in situ hybridization methods as well.
To initiate the analyte retrieval process, the section is first
dewaxed and hydrated. The slide is then placed in 10 mM sodium
citrate buffer pH 6.0 in a dish or jar. A representative procedure
uses an 1100 W microwave and microwaves the slide at 100% power for
2 minutes followed by microwaving the slides using 20% power for 18
minutes after checking to be sure the slide remains covered in
liquid. The slide is then allowed to cool in the uncovered
container and then rinsed with distilled water. HIER may be used in
combination with an enzymatic digestion to improve the reactivity
of the target to immunochemical reagents.
One such enzymatic digestion protocol uses proteinase K. A 20
.mu.g/ml concentration of proteinase K is prepared in 50 mM Tris
Base, 1 mM EDTA, 0.5% Triton X-100, pH 8.0 buffer. The process
first involves dewaxing sections in 2 changes of xylene, 5 minutes
each. Then the sample is hydrated in 2 changes of 100% ethanol for
3 minutes each, 95% and 80% ethanol for 1 minute each, and then
rinsed in distilled water. Sections are covered with Proteinase K
working solution and incubated 10-20 minutes at 37.degree. C. in
humidified chamber (optimal incubation time may vary depending on
tissue type and degree of fixation). The sections are cooled at
room temperature for 10 minutes and then rinsed in PBS Tween 20 for
2.times.2 mM If desired, sections can be blocked to eliminate
potential interference from endogenous compounds and enzymes. The
section is then incubated with primary antibody at appropriate
dilution in primary antibody dilution buffer for 1 hour at room
temperature or overnight at 4.degree. C. The section is then rinsed
with PBS Tween 20 for 2.times.2 mM Additional blocking can be
performed, if required for the specific application, followed by
additional rinsing with PBS Tween 20 for 3.times.2 mM and then
finally the immunostaining protocol completed.
A simple treatment with 1% SDS at room temperature has also been
demonstrated to improve immunohistochemical staining. Analyte
retrieval methods have been applied to slide mounted sections as
well as free floating sections. Another treatment option is to
place the slide in a jar containing citric acid and 0.1 Nonident
P40 at pH 6.0 and heating to 95.degree. C. The slide is then washed
with a buffer solution like PBS.
For immunological staining of tissues it may be useful to block
non-specific association of the antibody with tissue proteins by
soaking the section in a protein solution like serum or non-fat dry
milk.
Blocking reactions may include the need to reduce the level of
endogenous biotin; eliminate endogenous charge effects; inactivate
endogenous nucleases; and/or inactivate endogenous enzymes like
peroxidase and alkaline phosphatase. Endogenous nucleases may be
inactivated by degradation with proteinase K, by heat treatment,
use of a chelating agent such as EDTA or EGTA, the introduction of
carrier DNA or RNA, treatment with a chaotrope such as urea,
thiourea, guanidine hydrochloride, guanidine thiocyanate, lithium
perchlorate, etc, or diethyl pyrocarbonate. Alkaline phosphatase
may be inactivated by treated with 0.1N HCl for 5 minutes at room
temperature or treatment with 1 mM levamisole. Peroxidase activity
may be eliminated by treatment with 0.03% hydrogen peroxide.
Endogenous biotin may be blocked by soaking the slide or section in
an avidin (streptavidin, neutravidin may be substituted) solution
for at least 15 minutes at room temperature. The slide or section
is then washed for at least 10 minutes in buffer. This may be
repeated at least three times. Then the slide or section is soaked
in a biotin solution for 10 minutes. This may be repeated at least
three times with a fresh biotin solution each time. The buffer wash
procedure is repeated. Blocking protocols should be minimized to
prevent damaging either the cell or tissue structure or the target
or targets of interest but one or more of these protocols could be
combined to "block" a slide or section prior to reaction with one
or more slow off-rate aptamers. See Basic Medical Histology: the
Biology of Cells, Tissues and Organs, authored by Richard G.
Kessel, Oxford University Press, 1998.
Determination of Biomarker Values Using Mass Spectrometry
Methods
A variety of configurations of mass spectrometers can be used to
detect biomarker values. Several types of mass spectrometers are
available or can be produced with various configurations. In
general, a mass spectrometer has the following major components: a
sample inlet, an ion source, a mass analyzer, a detector, a vacuum
system, and instrument-control system, and a data system.
Difference in the sample inlet, ion source, and mass analyzer
generally define the type of instrument and its capabilities. For
example, an inlet can be a capillary-column liquid chromatography
source or can be a direct probe or stage such as used in
matrix-assisted laser desorption. Common ion sources are, for
example, electrospray, including nanospray and microspray or
matrix-assisted laser desorption. Common mass analyzers include a
quadrupole mass filter, ion trap mass analyzer and time-of-flight
mass analyzer. Additional mass spectrometry methods are well known
in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998);
Kinter and Sherman. Protein sequencing and identification using
tandem mass spectrometry. New York: Wiley-Interscience (2000).
Protein biomarkers and biomarker values can be detected and
measured by any of the following: electrospray ionization mass
spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted
laser desorption ionization time-of-flight mass spectrometry
(MALDI-TOF-MS), surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry (SELDI-TOF-MS),
desorption/ionization on silicon (DIOS), secondary ion mass
spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem
time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF,
atmospheric pressure chemical ionization mass spectrometry
(APCI-MS), APCI-MS/MS, APCI-(MS).sup.N, atmospheric pressure
photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and
APPI-(MS).sup.N, quadrupole mass spectrometry, Fourier transform
mass spectrometry (FTMS), quantitative mass spectrometry, and ion
trap mass spectrometry.
Sample preparation strategies are used to label and enrich samples
before mass spectroscopic characterization of protein biomarkers
and determination biomarker values. Labeling methods include but
are not limited to isobaric tag for relative and absolute
quantitation (iTRAQ) and stable isotope labeling with amino acids
in cell culture (SILAC). Capture reagents used to selectively
enrich samples for candidate biomarker proteins prior to mass
spectroscopic analysis include but are not limited to aptamers,
antibodies, nucleic acid probes, chimeras, small molecules, an
F(ab').sub.2 fragment, a single chain antibody fragment, an Fv
fragment, a single chain Fv fragment, a nucleic acid, a lectin, a
ligand-binding receptor, affybodies, nanobodies, ankyrins, domain
antibodies, alternative antibody scaffolds (e.g. diabodies etc)
imprinted polymers, avimers, peptidomimetics, peptoids, peptide
nucleic acids, threose nucleic acid, a hormone receptor, a cytokine
receptor, and synthetic receptors, and modifications and fragments
of these.
The foregoing assays enable the detection of biomarker values that
are useful in methods for diagnosing lung cancer, where the methods
comprise detecting, in a biological sample from an individual, at
least N biomarker values that each correspond to a biomarker
selected from the group consisting of the biomarkers provided in
Tables 18, 20 or 21, wherein a classification, as described in
detail below, using the biomarker values indicates whether the
individual has lung cancer. While certain of the described lung
cancer biomarkers are useful alone for detecting and diagnosing
lung cancer, methods are also described herein for the grouping of
multiple subsets of the lung cancer biomarkers that are each useful
as a panel of three or more biomarkers. Thus, various embodiments
of the instant application provide combinations comprising N
biomarkers, wherein N is at least three biomarkers. In other
embodiments, N is selected to be any number from 2-86 biomarkers.
It will be appreciated that N can be selected to be any number from
any of the above described ranges, as well as similar, but higher
order, ranges. In accordance with any of the methods described
herein, biomarker values can be detected and classified
individually or they can be detected and classified collectively,
as for example in a multiplex assay format.
In another aspect, methods are provided for detecting an absence of
lung cancer, the methods comprising detecting, in a biological
sample from an individual, at least N biomarker values that each
correspond to a biomarker selected from the group consisting of the
biomarkers provided in Tables 18, 20 or 21, wherein a
classification, as described in detail below, of the biomarker
values indicates an absence of lung cancer in the individual. While
certain of the described lung cancer biomarkers are useful alone
for detecting and diagnosing the absence of lung cancer, methods
are also described herein for the grouping of multiple subsets of
the lung cancer biomarkers that are each useful as a panel of three
or more biomarkers. Thus, various embodiments of the instant
application provide combinations comprising N biomarkers, wherein N
is at least three biomarkers. In other embodiments, N is selected
to be any number from 2-86 biomarkers. It will be appreciated that
N can be selected to be any number from any of the above described
ranges, as well as similar, but higher order, ranges. In accordance
with any of the methods described herein, biomarker values can be
detected and classified individually or they can be detected and
classified collectively, as for example in a multiplex assay
format.
Classification of Biomarkers and Calculation of Disease Scores
A biomarker "signature" for a given diagnostic test contains a set
of markers, each marker having different levels in the populations
of interest. Different levels, in this context, may refer to
different means of the marker levels for the individuals in two or
more groups, or different variances in the two or more groups, or a
combination of both. For the simplest form of a diagnostic test,
these markers can be used to assign an unknown sample from an
individual into one of two groups, either diseased or not diseased.
The assignment of a sample into one of two or more groups is known
as classification, and the procedure used to accomplish this
assignment is known as a classifier or a classification method.
Classification methods may also be referred to as scoring methods.
There are many classification methods that can be used to construct
a diagnostic classifier from a set of biomarker values. In general,
classification methods are most easily performed using supervised
learning techniques where a data set is collected using samples
obtained from individuals within two (or more, for multiple
classification states) distinct groups one wishes to distinguish.
Since the class (group or population) to which each sample belongs
is known in advance for each sample, the classification method can
be trained to give the desired classification response. It is also
possible to use unsupervised learning techniques to produce a
diagnostic classifier.
Common approaches for developing diagnostic classifiers include
decision trees; bagging+boosting+forests; rule inference based
learning; Parzen Windows; linear models; logistic; neural network
methods; unsupervised clustering; K-means; hierarchical
ascending/descending; semi-supervised learning; prototype methods;
nearest neighbor; kernel density estimation; support vector
machines; hidden Markov models; Boltzmann Learning; and classifiers
may be combined either simply or in ways which minimize particular
objective functions. For a review, see, e.g., Pattern
Classification, R. O. Duda, et al., editors, John Wiley & Sons,
2nd edition, 2001; see also, The Elements of Statistical
Learning--Data Mining, Inference, and Prediction, T. Hastie, et
al., editors, Springer Science+Business Media, LLC, 2nd edition,
2009; each of which is incorporated by reference in its
entirety.
To produce a classifier using supervised learning techniques, a set
of samples called training data are obtained. In the context of
diagnostic tests, training data includes samples from the distinct
groups (classes) to which unknown samples will later be assigned.
For example, samples collected from individuals in a control
population and individuals in a particular disease population can
constitute training data to develop a classifier that can classify
unknown samples (or, more particularly, the individuals from whom
the samples were obtained) as either having the disease or being
free from the disease. The development of the classifier from the
training data is known as training the classifier. Specific details
on classifier training depend on the nature of the supervised
learning technique. For purposes of illustration, an example of
training a naive Bayesian classifier will be described below (see,
e.g., Pattern Classification, R. O. Duda, et al., editors, John
Wiley & Sons, 2nd edition, 2001; see also, The Elements of
Statistical Learning--Data Mining, Inference, and Prediction, T.
Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd
edition, 2009).
Since typically there are many more potential biomarker values than
samples in a training set, care must be used to avoid over-fitting.
Over-fitting occurs when a statistical model describes random error
or noise instead of the underlying relationship. Over-fitting can
be avoided in a variety of way, including, for example, by limiting
the number of markers used in developing the classifier, by
assuming that the marker responses are independent of one another,
by limiting the complexity of the underlying statistical model
employed, and by ensuring that the underlying statistical model
conforms to the data.
An illustrative example of the development of a diagnostic test
using a set of biomarkers includes the application of a naive Bayes
classifier, a simple probabilistic classifier based on Bayes
theorem with strict independent treatment of the biomarkers. Each
biomarker is described by a class-dependent probability density
function (pdf) for the measured RFU values or log RFU (relative
fluorescence units) values in each class. The joint pdfs for the
set of markers in one class is assumed to be the product of the
individual class-dependent pdfs for each biomarker. Training a
naive Bayes classifier in this context amounts to assigning
parameters ("parameterization") to characterize the class dependent
pdfs. Any underlying model for the class-dependent pdfs may be
used, but the model should generally conform to the data observed
in the training set.
Specifically, the class-dependent probability of measuring a value
x.sub.i for biomarker i in the disease class is written as
p(x.sub.i|d) and the overall naive Bayes probability of observing n
markers with values
.times..times. ##EQU00001## is written as
.function..times..function. ##EQU00002## where the individual
x.sub.i is are the measured biomarker levels in RFU or log RFU. The
classification assignment for an unknown is facilitated by
calculating the probability of being diseased
.function. ##EQU00003## having measured
##EQU00004## compared to the probability of being disease free
(control)
.function. ##EQU00005## for the same measured values. The ratio of
these probabilities is computed from the class-dependent pdfs by
application of Bayes theorem,
.function..function..function..times..function..function..times..function-
. ##EQU00006## where P(d) is the prevalence of the disease in the
population appropriate to the test. Taking the logarithm of both
sides of this ratio and substituting the naive Bayes
class-dependent probabilities from above gives ln
.function..function..times..times..times..function..function..times..time-
s..function..function. ##EQU00007## This form is known as the log
likelihood ratio and simply states that the log likelihood of being
free of the particular disease versus having the disease and is
primarily composed of the sum of individual log likelihood ratios
of the n individual biomarkers. In its simplest form, an unknown
sample (or, more particularly, the individual from whom the sample
was obtained) is classified as being free of the disease if the
above ratio is greater than zero and having the disease if the
ratio is less than zero.
In one exemplary embodiment, the class-dependent biomarker pdfs
p(x.sub.i|c) and p(x.sub.i|d) are assumed to be normal or
log-normal distributions in the measured RFU values x.sub.i,
.times..function..times..pi..times..sigma..times..mu..times..sigma.
##EQU00008## with a similar expression for p(x.sub.i|d) with
.mu..sub.d,i and .sigma..sub.d,i.sup.2. Parameterization of the
model requires estimation of two parameters for each
class-dependent pdf, a mean p, and a variance .sigma..sup.2, from
the training data. This may be accomplished in a number of ways,
including, for example, by maximum likelihood estimates, by
least-squares, and by any other methods known to one skilled in the
art. Substituting the normal distributions for p(x.sub.i|c) and
p(x.sub.i|d) into the log-likelihood ratio defined above gives the
following expression:
.times..times..function..function..times..times..times..sigma..sigma..tim-
es..times..mu..sigma..mu..sigma..times..times..function..function.
##EQU00009## Once a set of .mu.s and .sigma..sup.2s have been
defined for each pdf in each class from the training data and the
disease prevalence in the population is specified, the Bayes
classifier is fully determined and may be used to classify unknown
samples with measured values x.
The performance of the naive Bayes classifier is dependent upon the
number and quality of the biomarkers used to construct and train
the classifier. A single biomarker will perform in accordance with
its KS-distance (Kolmogorov-Smirnov), as defined in Example 3,
below. If a classifier performance metric is defined as the sum of
the sensitivity (fraction of true positives, f.sub.TP) and
specificity (one minus the fraction of false positives,
1-f.sub.FP), a perfect classifier will have a score of two and a
random classifier, on average, will have a score of one. Using the
definition of the KS-distance, that value x* which maximizes the
difference in the cdf functions can be found by solving
.differential..differential..differential..function..function..differenti-
al. ##EQU00010## for x which leads to p(x*|c)=p(x*|d), i.e, the KS
distance occurs where the class-dependent pdfs cross. Substituting
this value of x* into the expression for the KS-distance yields the
following definition for
.times..function..function..times..intg..infin..times..function..times..i-
ntg..infin..times..function..times..times..intg..infin..times..function..t-
imes..intg..infin..times..function..times..times. ##EQU00011## the
KS distance is one minus the total fraction of errors using a test
with a cut-off at x*, essentially a single analyte Bayesian
classifier. Since we define a score of
sensitivity+specificity=2-f.sub.FP-f.sub.FN, combining the above
definition of the KS-distance we see that
sensitivity+specificity=1+KS. We select biomarkers with a statistic
that is inherently suited for building naive Bayes classifiers.
The addition of subsequent markers with good KS distances (>0.3,
for example) will, in general, improve the classification
performance if the subsequently added markers are independent of
the first marker. Using the sensitivity plus specificity as a
classifier score, it is straightforward to generate many high
scoring classifiers with a variation of a greedy algorithm. (A
greedy algorithm is any algorithm that follows the problem solving
metaheuristic of making the locally optimal choice at each stage
with the hope of finding the global optimum.)
The algorithm approach used here is described in detail in Example
4. Briefly, all single analyte classifiers are generated from a
table of potential biomarkers and added to a list. Next, all
possible additions of a second analyte to each of the stored single
analyte classifiers is then performed, saving a predetermined
number of the best scoring pairs, say, for example, a thousand, on
a new list. All possible three marker classifiers are explored
using this new list of the best two-marker classifiers, again
saving the best thousand of these. This process continues until the
score either plateaus or begins to deteriorate as additional
markers are added. Those high scoring classifiers that remain after
convergence can be evaluated for the desired performance for an
intended use. For example, in one diagnostic application,
classifiers with a high sensitivity and modest specificity may be
more desirable than modest sensitivity and high specificity. In
another diagnostic application, classifiers with a high specificity
and a modest sensitivity may be more desirable. The desired level
of performance is generally selected based upon a trade-off that
must be made between the number of false positives and false
negatives that can each be tolerated for the particular diagnostic
application. Such trade-offs generally depend on the medical
consequences of an error, either false positive or false
negative.
Various other techniques are known in the art and may be employed
to generate many potential classifiers from a list of biomarkers
using a naive Bayes classifier. In one embodiment, what is referred
to as a genetic algorithm can be used to combine different markers
using the fitness score as defined above. Genetic algorithms are
particularly well suited to exploring a large diverse population of
potential classifiers. In another embodiment, so-called ant colony
optimization can be used to generate sets of classifiers. Other
strategies that are known in the art can also be employed,
including, for example, other evolutionary strategies as well as
simulated annealing and other stochastic search methods.
Metaheuristic methods, such as, for example, harmony search may
also be employed.
Exemplary embodiments use any number of the lung cancer biomarkers
listed in Tables 18, 20 or 21 in various combinations to produce
diagnostic tests for detecting lung cancer (see Examples 2 and 6
for a detailed description of how these biomarkers were
identified). In one embodiment, a method for diagnosing lung cancer
uses a naive Bayes classification method in conjunction with any
number of the lung cancer biomarkers listed in Tables 18, 20 or 21.
In an illustrative example (Example 3), the simplest test for
detecting lung cancer from a population of asymptomatic smokers can
be constructed using a single biomarker, for example, SCFsR which
is down-regulated in lung cancer with a KS-distance of 0.37
(1+KS=1.37). Using the parameters .mu..sub.c,i, .sigma..sub.d,i and
.sigma..sub.d,i for SCFsR from Table 15 and the equation for the
log-likelihood described above, a diagnostic test with a
sensitivity of 63% and specificity of 73%
(sensitivity+specificity=1.36) can be produced, see Table 14. The
ROC curve for this test is displayed in FIG. 2 and has an AUC of
0.75.
Addition of biomarker HSP90a, for example, with a KS-distance of
0.5, significantly improves the classifier performance to a
sensitivity of 76% and specificity of 0.75%
(sensitivity+specificity=1.51) and an AUC=0.84. Note that the score
for a classifier constructed of two biomarkers is not a simple sum
of the KS-distances; KS-distances are not additive when combining
biomarkers and it takes many more weak markers to achieve the same
level of performance as a strong marker. Adding a third marker,
ERBB1, for example, boosts the classifier performance to 78%
sensitivity and 83% specificity and AUC=0.87. Adding additional
biomarkers, such as, for example, PTN, BTK, CD30, Kallikrein 7,
LRIG3, LDH-H1, and PARC, produces a series of lung cancer tests
summarized in Table 14 and displayed as a series of ROC curves in
FIG. 3. The score of the classifiers as a function of the number of
analytes used in classifier construction is displayed in FIG. 4.
The sensitivity and specificity of this exemplary ten-marker
classifier is >87% and the AUC is 0.91.
The markers listed in Tables 18, 20 or 21 can be combined in many
ways to produce classifiers for diagnosing lung cancer. In some
embodiments, panels of biomarkers are comprised of different
numbers of analytes depending on a specific diagnostic performance
criterion that is selected. For example, certain combinations of
biomarkers will produce tests that are more sensitive (or more
specific) than other combinations.
Once a panel is defined to include a particular set of biomarkers
from Tables 18, 20 or 21 and a classifier is constructed from a set
of training data, the definition of the diagnostic test is
complete. In one embodiment, the procedure used to classify an
unknown sample is outlined in FIG. 1A. In another embodiment the
procedure used to classify an unknown sample is outlined in FIG.
1B. The biological sample is appropriately diluted and then run in
one or more assays to produce the relevant quantitative biomarker
levels used for classification. The measured biomarker levels are
used as input for the classification method that outputs a
classification and an optional score for the sample that reflects
the confidence of the class assignment.
Table 21 identifies eighty-six biomarkers that are useful for
diagnosing lung cancer in both tissue and blood samples. Table 20
identifies twenty-five biomarkers that were identified in tissue
samples, but which are useful in serum and plasma samples as well.
This is a surprisingly larger number than expected when compared to
what is typically found during biomarker discovery efforts and may
be attributable to the scale of the described study, which
encompassed over 800 proteins measured in hundreds of individual
samples, in some cases at concentrations in the low femtomolar
range. Presumably, the large number of discovered biomarkers
reflects the diverse biochemical pathways implicated in both tumor
biology and the body's response to the tumor's presence; each
pathway and process involves many proteins. The results show that
no single protein of a small group of proteins is uniquely
informative about such complex processes; rather, that multiple
proteins are involved in relevant processes, such as apoptosis or
extracellular matrix repair, for example.
Given the numerous biomarkers identified during the described
study, one would expect to be able to derive large numbers of
high-performing classifiers that can be used in various diagnostic
methods. To test this notion, tens of thousands of classifiers were
evaluated using the biomarkers in Table 1. As described in Example
4, many subsets of the biomarkers presented in Table 1 can be
combined to generate useful classifiers. By way of example,
descriptions are provided for classifiers containing 1, 2, and 3
biomarkers for each of two uses: lung cancer screening of smokers
at high risk and diagnosis of individuals that have pulmonary
nodules that are detectable by CT. As described in Example 4, all
classifiers that were built using the biomarkers in Table 1 perform
distinctly better than classifiers that were built using
"non-markers".
The performance of classifiers obtained by randomly excluding some
of the markers in Table 1, which resulted in smaller subsets from
which to build the classifiers, was also tested. As described in
Example 4, Part 3, the classifiers that were built from random
subsets of the markers in Table 1 performed similarly to optimal
classifiers that were built using the full list of markers in Table
1.
The performance of ten-marker classifiers obtained by excluding the
"best" individual markers from the ten-marker aggregation was also
tested. As described in Example 4, Part 3, classifiers constructed
without the "best" markers in Table 1 also performed well. Many
subsets of the biomarkers listed in Table 1 performed close to
optimally, even after removing the top 15 of the markers listed in
the Table. This implies that the performance characteristics of any
particular classifier are likely not due to some small core group
of biomarkers and that the disease process likely impacts numerous
biochemical pathways, which alters the expression level of many
proteins.
The results from Example 4 suggest certain possible conclusions:
First, the identification of a large number of biomarkers enables
their aggregation into a vast number of classifiers that offer
similarly high performance Second, classifiers can be constructed
such that particular biomarkers may be substituted for other
biomarkers in a manner that reflects the redundancies that
undoubtedly pervade the complexities of the underlying disease
processes. That is to say, the information about the disease
contributed by any individual biomarker identified in Table 1
overlaps with the information contributed by other biomarkers, such
that it may be that no particular biomarker or small group of
biomarkers in Table 1 must be included in any classifier.
Exemplary embodiments use naive Bayes classifiers constructed from
the data in Tables 38 and 39 to classify an unknown sample. The
procedure is outlined in FIGS. 1A and B. In one embodiment, the
biological sample is optionally diluted and run in a multiplexed
aptamer assay. The data from the assay are normalized and
calibrated as outlined in Example 3, and the resulting biomarker
levels are used as input to a Bayes classification scheme. The
log-likelihood ratio is computed for each measured biomarker
individually and then summed to produce a final classification
score, which is also referred to as a diagnostic score. The
resulting assignment as well as the overall classification score
can be reported. Optionally, the individual log-likelihood risk
factors computed for each biomarker level can be reported as well.
The details of the classification score calculation are presented
in Example 3.
To demonstrate the utility of aptamer-based proteomic technology
described herein for use in discovery of disease-related biomarkers
from tissues, homogenized tissues samples from surgical resections
obtained from eight non-small cell lung cancer (NSCLC) patients
were analyzed, as described in Example 6. All NSCLC patients were
smokers, ranging in age from 47 to 75 years old and covering NSCLC
stages 1A through 3B (Table 17). Three samples were obtained from
each resection: tumor tissue sample, adjacent non-tumor tissue.
Total protein concentration was adjusted and normalized in each
homogenate for proteomic profiling followed by analysis the DNA
microarray platform to measure the concentrations of over 800 human
proteins (see Gold et al., "Aptamer-based multiplexed proteomic
technology for biomarker discovery," Nature Precedings (2010)).
The protein concentration measurements, expressed as relative
fluorescence units (RFU), allow large-scale comparisons of protein
signatures among samples (see FIG. 21). With reference to FIG. 21,
first the protein expression levels between the control adjacent
and distant tissues was compared for each patient sample (FIG.
21A). In this comparison, only one analyte (fibrinogen) exhibited
more than a two-fold difference between the samples. Overall, the
signals generated by most analytes were similar in adjacent and
distant tissue.
In contrast, comparison of tumor tissues with non-tumor tissue
(adjacent or distant) identified 11 (1.3%) proteins with greater
than four-fold differences and 53 (6.5%) proteins with greater than
two-fold differences (see FIGS. 21B and 21C). The remaining 767
(93.5%) proteins showed relatively smaller differences between
tumor and non-tumor tissue. Some proteins were substantially
suppressed while others were elevated in tumor tissues compared to
adjacent or distant tissues. Differential expression of proteins
between adjacent and tumor tissue, or between distal and tumor
tissue, was similar overall. Changes in distal tissue were
generally larger (FIG. 21), which demonstrates that most protein
changes are specific to the local tumor environment.
To identify NSCLC tissue biomarkers, protein expression levels
between tumor, adjacent and distant tissue samples were compared
using the Mann-Whitney test as described in Ostroff et al.,
"Unlocking biomarker discovery: Large scale application of aptamer
proteomic technology for early detection of lung cancer," Nature
Precedings, (2010)). Thirty-six proteins with the greatest fold
change and with statistically significant differences between tumor
and non-tumor tissue were identified with a false discovery rate
cutoff of q<0.05 for significance FIGS. 23 and 24, and Table
18). Twenty of these proteins were up-regulated and 16 were
down-regulated in tumor tissue. Although the number of samples used
for this study was relatively small, a powerful individual-based
study design in which each tumor sample had its own healthy tissue
controls was employed. This eliminates the population variance
associated with population-based study designs. The availability of
appropriately chosen reference samples is increasingly recognized
as a critically important component in biomarker discovery research
(Bossuyt (2011) J. Am. Med. Assoc. 305:2229-30; Ioannidis and
Panagiotou (2011) J. Am. Med. Assoc. 305:2200-10; Diamandis (2010)
J. Natl. Cancer Inst. 102:1462-7).
It is believed that approximately one-third (13/36) of the NSCLC
tissue biomarkers identified herein are novel. The remaining
two-thirds (23/36) have been reported previously as differentially
expressed proteins or genes in NSCLC tumor tissue (Table 18).
The biomarkers identified according to the method of Example 6 can
be classified broadly into four biological processes associated
with important hallmarks of tumor biology (Hanahan & Weinberg
(2011) Cell 144:646-74) as shown in Table 19: 1) angiogenesis, 2)
growth and metabolism, 3) inflammation and apoptosis, and 4)
invasion and metastasis. Admittedly, these are convenient albeit
inexact classifications that approximate a highly complex and
dynamic system in which these molecules often play multiple and
nuanced roles. Therefore, the specific state of a given system
ultimately affects the expression and function of any particular
molecule. Biological understanding is far from complete in these
systems. With the SOMAscan platform, the quantitative expression of
large numbers of proteins in various tissues and disease processes
is made possible. These data provide new coordinates to help map
the dynamics of these systems, which in turn will provide a more
complete understanding of the biology of lung cancer as well as
other diseases. The results from the current study provide a new
perspective on NSCLC tumor biology, with both familiar and new
elements.
Angiogenesis
Angiogenesis drives growth of new blood vessels to support tumor
growth and metabolism. The regulation of angiogenesis is a complex
biological phenomenon controlled by both positive and negative
signals (Hanahan & Weinberg, (2011) Cell 144:646-74). Among the
NSCLC tissue biomarkers identified in this study were well known
positive and negative angiogenesis regulators (FIGS. 23 and 24 and
Table 19), all of which have been observed previously in NSCLC
tumor tissue (Fontanini et al. (1999) British Journal of Cancer
79(2):363-369; Imoto et al. (1998) J. Thorac. Carciovasc. Surg
115:1007-1011; Ohta et al. (2006) Ann. Thorac. Surg. 82:1180-1184;
Iizasa et al. (2004) Clinical Cancer Research 10:5361-5366). These
include the prototypic angiogenesis inducer VEGF and inhibitors
endostatin and thrombospondin-1 (TSP-1). VEGF is a powerful growth
factor that promotes new blood vessel growth and was strongly
up-regulated in NSCLC tumor tissue, consistent with previous
observations (Imoto et al. (1998) J. Thorac. Carciovasc. Surg
115:1007-1011), and including our study of serum samples from NSCLC
patients (Ostroff et al., "Unlocking biomarker discovery: Large
scale application of aptamer proteomic technology for early
detection of lung cancer," Nature Precedings, (2010)). Endostatin
is a proteolytic fragment of Collagen XVIII and a strong inhibitor
of endothelial cell proliferation and angiogenesis (Iizasa et al.
(August 2004) Clinical Cancer Research 10:5361-5366). TSP-1 and the
related thrombospondin-2 (TSP-2) were substantially up-regulated in
NSCLC tumor tissue. TSP-1 and TSP-2 are extracellular matrix
proteins with complex, context-dependent effects modulated through
a variety of interactions with cell-surface receptors, growth
factors, cytokines, matrix metalloproteinases, and other molecules.
Archetypically in model systems, TSP-1 and TSP-2 inhibit
angiogenesis by inhibiting endothelial cell proliferation through
the CD47 receptor and inducing endothelial cell apoptosis through
the CD36 receptor. There is also evidence for proangiogenic
influences for TSP-1 and TSP-2 (Bornstein (2009) J. Cell Commun.
Signal. 3(3-4):189-200). Finally, reported TSP-1 and TSP-2 relative
and absolute expression levels in NSCLC tissue vary (Chijiwa et al.
(2009) Oncology Reports 22:279-283; Chen et al. (2009) J Int Med
Res 37:551-556; Oshika 1998, Fontanini et al. (1999) British
Journal of Cancer 79(2):363-369), likely due to their complex
functions. In this study, it was found that CD36 was down-regulated
in NSCLC tumor tissue, which could indicate an adaptation of tumor
cells reduce sensitivity to TSP-1 and TSP-2-mediated apoptosis.
Growth and Metabolism
Ten of the NSCLC biomarkers identified are associated with growth
and metabolism functions. Half of these biomarkers are involved in
the complex hormonal regulation of cellular growth and energy
metabolism. Three insulin-like growth factor binding proteins
(IGFBPs), which modulate the activity of insulin-like growth
factors (IGFs), were up-regulated in NSCLC tumors (IGFBP-2, -5, and
-7). Several reports have qualitatively assessed IGFBP-2, -5, and
-7 in NSCLC (Table 18) and suggest higher expression in NSCLC
tissue than in normal tissue. Insulin and IGFs are hormones that
strongly influence cellular growth and metabolism, and cancer cells
are often dependent on these molecules for growth and proliferation
(Robert et al. (August 1999) Clinical Cancer Research 5:2094-2102;
Liu et al. (June 2007) Lung Cancer 56(3):307-317; Singhal et al.
(2008) Lung Cancer 60:313-324). These hormones are in turn degraded
by insulysin, which we find up-regulated in NSCLC tumor tissue. The
hormone adiponectin controls lipid metabolism and insulin
sensitivity, and we found adiponectin down-regulated in NSCLC
tumors. The remaining five biomarkers, carbonic anhydrase III,
NAGK, TrATPase, tryptase .beta.-2, and MAPK13, are enzymes with
roles in cellular metabolism (Table 17).
Inflammation and Apoptosis
Inflammation and apoptosis are hallmarks of cancer biology, and a
number of potential biomarkers associated with these processes that
have been associated previously with NSCLC (Table 19). Caspase-3,
which has been associated with metastasis (Chen et al. (2010) Lung
Cancer (doi:1016/j.lungcan.2010.10.015), was found to be
up-regulated in NSCLC tumor tissue. Another notable example is
RAGE, which has been reported to be dramatically down-regulated in
NSCLC tissue (Jing et al. (2010) Neoplasma. 57:55-61, Bartling et
al. (2005) Carcinogenesis 26:293-301). This finding is consistent
with the measurement disclosed herein, in which sRAGE had the
largest observed change for proteins that are lower in tumor than
in non-malignant tissue. Although not limited by theory, one
hypothesis is that RAGE plays a role in epithelial organization,
and decreased levels of RAGE in lung tumors may contribute to loss
of epithelial tissue structure, potentially leading to malignant
transformation (Bartling et al. (2005) Carcinogenesis
26(2):293-301). Several chemokines, such as BCA-1, CXCL16, IL-8,
and NAP-2, are altered (Table 18), consistent with the hypothesis
that invasion of tumors with cells from the innate and adaptive
arms of the immune system provide bioactive molecules that affect
proliferative and angiogenic signals (Hanahan & Weinberg (2011)
Cell 144:646-74).
Invasion and Metastasis
The largest group of potential biomarkers contains proteins that
function in cell-cell and cell-matrix interactions and are involved
in invasion and metastasis. Many have been previously reported to
be associated with NSCLC. Most notable are two of the matrix
metalloproteases, MMP-7 and MMP-12, which contribute to proteolytic
degradation of extracellular matrix components and processing of
substrates such as growth factors (see e.g. Su et al. (2004)
Chinese Journal of Clinical Oncology 1(2):126-130; Wegmann et al.
(1993) Eur. J. Cancer 29A(11):1578-1584). Such processes are well
known to play a role in creating tumor microenvironments. It was
found that both MMP-7 and MMP-12 were up-regulated in NSCLC tissue
(Table 18), which is consistent with similar study that used
antibody-based measurements (Shah et al. (2010) The Journal of
Thoracic and Cardiovascular Surgery 139(4):984-990). The
over-expression of MMP-7 and MMP-12 has been associated with poor
prognosis in NSCLC (Shah et al. (2010) The Journal of Thoracic and
Cardiovascular Surgery 139(4):984-990). MMP-12 levels have been
correlated with local recurrence and metastatic disease (Hofmann et
al. (2005) Clin. Cancer Res. 11:1086-92, Hoffman et al. (2006)
Oncol. Rep. 16:587-95).). Two of the eight subjects studied had
normal levels of MMP-12, whereas the other six had 15-50.times.
elevation of MMP-12 in tumor tissue compared to non-tumor
tissue.
Performance of NSCLC Biomarkers as Histochemistry Probes
An understanding of the differences in protein expression between
tumor and non-tumor tissues can be used to identify novel
histochemistry probes. Such probes can enable more precise
molecular characterization of tumors and their effects on the
surrounding stroma. FIG. 25 demonstrates the ability of two of the
identified SOMAmers to stain fresh frozen tissues obtained from the
same tumor resections used for the discovery of these biomarkers.
Thrombospondin-2 (TSP2) was found to be increased in tumor tissue
homogenates while macrophage mannose receptor (MRC1) was decreased.
Tissue staining with these SOMAmers was consistent with the
profiling results. Additional examples, as well as antibody
confirmation of staining patterns, are shown in FIG. 27.
Comparison of NSCLC Tissue and Serum Biomarkers
Differential expression of proteins in sera of NSCLC patients
relative to cancer-free controls compared with that of NSCLC tissue
samples yields useful insights (FIG. 26). The most striking
observation is that relative changes in protein expression are
greater in tissues than in serum. This result could be expected
since tumor tissue is the source of the changes in protein
expression that is then, even if fully released into circulation,
diluted many-fold into total volume of blood. This trend is evident
in the elongated distribution of data points along the x-axis in
FIG. 26 in which axes are drawn on the same scale to illustrate
this point. Twelve of the analytes shown in FIGS. 23 and 24 as
altered in tumor tissue are also differentially expressed in sera
from NSCLC patients vs. controls (filled red circles in FIG. 26).
Most of the directional changes are the same between tissue and
sera, but a few are not. Local concentrations of proteins in a
tissue homogenate clearly need not correlate with circulating
levels of the proteins and inverse correlations may provide clues
regarding the redistribution of certain biomarkers in diseased
versus normal tissues.
The discovery of novel biomarkers with demonstrable diagnostic or
clinical utility has been a considerable challenge in recent years
(Diamandis (2010) J. Natl, Cancer Inst. 102:1462-7). The reasons
for this include the omnipresence of pre-analytical and analytical
artifacts, unavailability of suitable healthy-state controls and
unsophisticated study designs, and the difficulty of detecting
small changes in protein levels at very low concentrations. This
challenge is especially pronounced with cancer biomarkers where the
objective is often to identify a tiny malignancy in a relatively
large human body at an early stage. With regard to the later point,
one way to improve the chances of discovering true cancer
biomarkers is to obtain protein expression data from both the
source of the disease, such as tumor tissue, as well as from the
circulation. The combined results can partially corroborate the
validity of potential biomarkers. The instant application
demonstrates that this is possible with the disclosed highly
multiplexed and sensitive proteomic assay. It has been shown that
tissues, like plasma or serum, are also amenable to SOMAscan, and
the resulting comparative analysis of protein expression in NSCLC
tumor tissues with surrounding healthy lung tissues offers a
complement to the existing dataset of potential NSCLC biomarkers
identified from serum samples (see U.S. Pub. No. 2010/0070191). In
the instant case, one third, or twelve of the thirty-six tissue
biomarkers reported herein (BCA-1 (BCL), cadherin-1 (cadherin-E),
catalase, endostatin, IGFBP-2, MRC1 (macrophage mannose receptor),
MAPK-13 (MK13), MMP-7, MMP-12, NAGK, VEGF and YES have been
previously identified in serum. Taken together, these data
contribute to further understanding of the complexity of changes
accompanying NSCLC and provide additional potential biomarkers for
the early detection of this deadly disease.
Kits
Any combination of the biomarkers of Table 20 (as well as
additional biomedical information) can be detected using a suitable
kit, such as for use in performing the methods disclosed herein.
Furthermore, any kit can contain one or more detectable labels as
described herein, such as a fluorescent moiety, etc.
In one embodiment, a kit includes (a) one or more capture reagents
(such as, for example, at least one aptamer or antibody) for
detecting one or more biomarkers in a biological sample, wherein
the biomarkers include any of the biomarkers set forth in Tables
18, 20 or 21 and optionally (b) one or more software or computer
program products for classifying the individual from whom the
biological sample was obtained as either having or not having lung
cancer or for determining the likelihood that the individual has
lung cancer, as further described herein. Alternatively, rather
than one or more computer program products, one or more
instructions for manually performing the above steps by a human can
be provided.
The combination of a solid support with a corresponding capture
reagent and a signal generating material is referred to herein as a
"detection device" or "kit". The kit can also include instructions
for using the devices and reagents, handling the sample, and
analyzing the data. Further the kit may be used with a computer
system or software to analyze and report the result of the analysis
of the biological sample.
The kits can also contain one or more reagents (e.g.,
solubilization buffers, detergents, washes, or buffers) for
processing a biological sample. Any of the kits described herein
can also include, e.g., buffers, blocking agents, mass spectrometry
matrix materials, antibody capture agents, positive control
samples, negative control samples, software and information such as
protocols, guidance and reference data.
In one aspect, the invention provides kits for the analysis of lung
cancer status. The kits include PCR primers for one or more
biomarkers selected from Tables 18, 20, or 21. The kit may further
include instructions for use and correlation of the biomarkers with
lung cancer. The kit may also include a DNA array containing the
complement of one or more of the biomarkers selected from Table 20,
reagents, and/or enzymes for amplifying or isolating sample DNA.
The kits may include reagents for real-time PCR, for example,
TaqMan probes and/or primers, and enzymes.
For example, a kit can comprise (a) reagents comprising at least
capture reagent for quantifying one or more biomarkers in a test
sample, wherein said biomarkers comprise the biomarkers set forth
in Tables 18, 20, or 21, or any other biomarkers or biomarkers
panels described herein, and optionally (b) one or more algorithms
or computer programs for performing the steps of comparing the
amount of each biomarker quantified in the test sample to one or
more predetermined cutoffs and assigning a score for each biomarker
quantified based on said comparison, combining the assigned scores
for each biomarker quantified to obtain a total score, comparing
the total score with a predetermined score, and using said
comparison to determine whether an individual has lung cancer.
Alternatively, rather than one or more algorithms or computer
programs, one or more instructions for manually performing the
above steps by a human can be provided.
Computer Methods and Software
Once a biomarker or biomarker panel is selected, a method for
diagnosing an individual can comprise the following: 1) collect or
otherwise obtain a biological sample; 2) perform an analytical
method to detect and measure the biomarker or biomarkers in the
panel in the biological sample; 3) perform any data normalization
or standardization required for the method used to collect
biomarker values; 4) calculate the marker score; 5) combine the
marker scores to obtain a total diagnostic score; and 6) report the
individual's diagnostic score. In this approach, the diagnostic
score may be a single number determined from the sum of all the
marker calculations that is compared to a preset threshold value
that is an indication of the presence or absence of disease. Or the
diagnostic score may be a series of bars that each represent a
biomarker value and the pattern of the responses may be compared to
a pre-set pattern for determination of the presence or absence of
disease.
At least some embodiments of the methods described herein can be
implemented with the use of a computer. An example of a computer
system 100 is shown in FIG. 6. With reference to FIG. 6, system 100
is shown comprised of hardware elements that are electrically
coupled via bus 108, including a processor 101, input device 102,
output device 103, storage device 104, computer-readable storage
media reader 105a, communications system 106 processing
acceleration (e.g., DSP or special-purpose processors) 107 and
memory 109. Computer-readable storage media reader 105a is further
coupled to computer-readable storage media 105b, the combination
comprehensively representing remote, local, fixed and/or removable
storage devices plus storage media, memory, etc. for temporarily
and/or more permanently containing computer-readable information,
which can include storage device 104, memory 109 and/or any other
such accessible system 100 resource. System 100 also comprises
software elements (shown as being currently located within working
memory 191) including an operating system 192 and other code 193,
such as programs, data and the like.
With respect to FIG. 6, system 100 has extensive flexibility and
configurability. Thus, for example, a single architecture might be
utilized to implement one or more servers that can be further
configured in accordance with currently desirable protocols,
protocol variations, extensions, etc. However, it will be apparent
to those skilled in the art that embodiments may well be utilized
in accordance with more specific application requirements. For
example, one or more system elements might be implemented as
sub-elements within a system 100 component (e.g., within
communications system 106). Customized hardware might also be
utilized and/or particular elements might be implemented in
hardware, software or both. Further, while connection to other
computing devices such as network input/output devices (not shown)
may be employed, it is to be understood that wired, wireless,
modem, and/or other connection or connections to other computing
devices might also be utilized.
In one aspect, the system can comprise a database containing
features of biomarkers characteristic of lung cancer. The biomarker
data (or biomarker information) can be utilized as an input to the
computer for use as part of a computer implemented method. The
biomarker data can include the data as described herein.
In one aspect, the system further comprises one or more devices for
providing input data to the one or more processors.
The system further comprises a memory for storing a data set of
ranked data elements.
In another aspect, the device for providing input data comprises a
detector for detecting the characteristic of the data element,
e.g., such as a mass spectrometer or gene chip reader.
The system additionally may comprise a database management system.
User requests or queries can be formatted in an appropriate
language understood by the database management system that
processes the query to extract the relevant information from the
database of training sets.
The system may be connectable to a network to which a network
server and one or more clients are connected. The network may be a
local area network (LAN) or a wide area network (WAN), as is known
in the art. Preferably, the server includes the hardware necessary
for running computer program products (e.g., software) to access
database data for processing user requests.
The system may include an operating system (e.g., UNIX or Linux)
for executing instructions from a database management system. In
one aspect, the operating system can operate on a global
communications network, such as the internet, and utilize a global
communications network server to connect to such a network.
The system may include one or more devices that comprise a
graphical display interface comprising interface elements such as
buttons, pull down menus, scroll bars, fields for entering text,
and the like as are routinely found in graphical user interfaces
known in the art. Requests entered on a user interface can be
transmitted to an application program in the system for formatting
to search for relevant information in one or more of the system
databases. Requests or queries entered by a user may be constructed
in any suitable database language.
The graphical user interface may be generated by a graphical user
interface code as part of the operating system and can be used to
input data and/or to display inputted data. The result of processed
data can be displayed in the interface, printed on a printer in
communication with the system, saved in a memory device, and/or
transmitted over the network or can be provided in the form of the
computer readable medium.
The system can be in communication with an input device for
providing data regarding data elements to the system (e.g.,
expression values). In one aspect, the input device can include a
gene expression profiling system including, e.g., a mass
spectrometer, gene chip or array reader, and the like.
The methods and apparatus for analyzing lung cancer biomarker
information according to various embodiments may be implemented in
any suitable manner, for example, using a computer program
operating on a computer system. A conventional computer system
comprising a processor and a random access memory, such as a
remotely-accessible application server, network server, personal
computer or workstation may be used. Additional computer system
components may include memory devices or information storage
systems, such as a mass storage system and a user interface, for
example a conventional monitor, keyboard and tracking device. The
computer system may be a stand-alone system or part of a network of
computers including a server and one or more databases.
The lung cancer biomarker analysis system can provide functions and
operations to complete data analysis, such as data gathering,
processing, analysis, reporting and/or diagnosis. For example, in
one embodiment, the computer system can execute the computer
program that may receive, store, search, analyze, and report
information relating to the lung cancer biomarkers. The computer
program may comprise multiple modules performing various functions
or operations, such as a processing module for processing raw data
and generating supplemental data and an analysis module for
analyzing raw data and supplemental data to generate a lung cancer
status and/or diagnosis. Diagnosing lung cancer status may comprise
generating or collecting any other information, including
additional biomedical information, regarding the condition of the
individual relative to the disease, identifying whether further
tests may be desirable, or otherwise evaluating the health status
of the individual.
Referring to FIG. 7, an example of a method of utilizing a computer
in accordance with principles of a disclosed embodiment can be
seen. In FIG. 7, a flowchart 3000 is shown. In block 3004,
biomarker information can be retrieved for an individual. The
biomarker information can be retrieved from a computer database,
for example, after testing of the individual's biological sample is
performed. The biomarker information can comprise biomarker values
that each correspond to one of at least N biomarkers selected from
a group consisting of the biomarkers provided in Table 18, wherein
N=2-36, Table 20, wherein N=2-25 or Table 21, wherein N=2-86. In
block 3008, a computer can be utilized to classify each of the
biomarker values. And, in block 3012, a determination can be made
as to the likelihood that an individual has lung cancer based upon
a plurality of classifications. The indication can be output to a
display or other indicating device so that it is viewable by a
person. Thus, for example, it can be displayed on a display screen
of a computer or other output device.
Referring to FIG. 8, an alternative method of utilizing a computer
in accordance with another embodiment can be illustrated via
flowchart 3200. In block 3204, a computer can be utilized to
retrieve biomarker information for an individual. The biomarker
information comprises a biomarker value corresponding to a
biomarker selected from the group of biomarkers provided in Tables
18, 20 or 21. In block 3208, a classification of the biomarker
value can be performed with the computer. And, in block 3212, an
indication can be made as to the likelihood that the individual has
lung cancer based upon the classification. The indication can be
output to a display or other indicating device so that it is
viewable by a person. Thus, for example, it can be displayed on a
display screen of a computer or other output device.
Some embodiments described herein can be implemented so as to
include a computer program product. A computer program product may
include a computer readable medium having computer readable program
code embodied in the medium for causing an application program to
execute on a computer with a database.
As used herein, a "computer program product" refers to an organized
set of instructions in the form of natural or programming language
statements that are contained on a physical media of any nature
(e.g., written, electronic, magnetic, optical or otherwise) and
that may be used with a computer or other automated data processing
system. Such programming language statements, when executed by a
computer or data processing system, cause the computer or data
processing system to act in accordance with the particular content
of the statements. Computer program products include without
limitation: programs in source and object code and/or test or data
libraries embedded in a computer readable medium. Furthermore, the
computer program product that enables a computer system or data
processing equipment device to act in pre-selected ways may be
provided in a number of forms, including, but not limited to,
original source code, assembly code, object code, machine language,
encrypted or compressed versions of the foregoing and any and all
equivalents.
In one aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers in the biological sample selected from the group of
biomarkers provided in Table 18, wherein N=2-36, Table 20, wherein
N=2-25 or Table 21, wherein N=2-86; and code that executes a
classification method that indicates a lung disease status of the
individual as a function of the biomarker values.
In still another aspect, a computer program product is provided for
indicating a likelihood of lung cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises a
biomarker value corresponding to a biomarker in the biological
sample selected from the group of biomarkers provided in Table 18,
wherein N=2-36, Table 20, wherein N=2-25 or Table 21, wherein
N-2-86; and code that executes a classification method that
indicates a lung disease status of the individual as a function of
the biomarker value.
While various embodiments have been described as methods or
apparatuses, it should be understood that embodiments can be
implemented through code coupled with a computer, e.g., code
resident on a computer or accessible by the computer. For example,
software and databases could be utilized to implement many of the
methods discussed above. Thus, in addition to embodiments
accomplished by hardware, it is also noted that these embodiments
can be accomplished through the use of an article of manufacture
comprised of a computer usable medium having a computer readable
program code embodied therein, which causes the enablement of the
functions disclosed in this description. Therefore, it is desired
that embodiments also be considered protected by this patent in
their program code means as well. Furthermore, the embodiments may
be embodied as code stored in a computer-readable memory of
virtually any kind including, without limitation, RAM, ROM,
magnetic media, optical media, or magneto-optical media. Even more
generally, the embodiments could be implemented in software, or in
hardware, or any combination thereof including, but not limited to,
software running on a general purpose processor, microcode, PLAs,
or ASICs.
It is also envisioned that embodiments could be accomplished as
computer signals embodied in a carrier wave, as well as signals
(e.g., electrical and optical) propagated through a transmission
medium. Thus, the various types of information discussed above
could be formatted in a structure, such as a data structure, and
transmitted as an electrical signal through a transmission medium
or stored on a computer readable medium.
It is also noted that many of the structures, materials, and acts
recited herein can be recited as means for performing a function or
step for performing a function. Therefore, it should be understood
that such language is entitled to cover all such structures,
materials, or acts disclosed within this specification and their
equivalents, including the matter incorporated by reference.
EXAMPLES
The following examples are provided for illustrative purposes only
and are not intended to limit the scope of the application as
defined by the appended claims. All examples described herein were
carried out using standard techniques, which are well known and
routine to those of skill in the art. Routine molecular biology
techniques described in the following examples can be carried out
as described in standard laboratory manuals, such as Sambrook et
al., Molecular Cloning: A Laboratory Manual, 3rd. ed., Cold Spring
Harbor Laboratory Press, Cold Spring Harbor, N.Y., (2001).
Example 1. Multiplexed Aptamer Analysis of Samples for Lung Cancer
Biomarker Selection
This example describes the multiplex aptamer assay used to analyze
the samples and controls for the identification of the biomarkers
set forth in Table 1, Col. 2 (see FIG. 9). In this case, the
multiplexed analysis utilized 820 aptamers, each unique to a
specific target.
In this method, pipette tips were changed for each solution
addition.
Also, unless otherwise indicated, most solution transfers and wash
additions used the 96-well head of a Beckman Biomek Fx.sup.P.
Method steps manually pipetted used a twelve channel P200
Pipetteman (Rainin Instruments, LLC, Oakland, Calif.), unless
otherwise indicated. A custom buffer referred to as SB17 was
prepared in-house, comprising 40 mM HEPES, 100 mM NaCl, 5 mM KCl, 5
mM MgCl.sub.2, 1 mM EDTA at pH7.5. All steps were performed at room
temperature unless otherwise indicated.
1. Preparation of Aptamer Stock Solution
For aptamers without a photo-cleavable biotin linker, custom stock
aptamer solutions for 10%, 1% and 0.03% serum were prepared at
8.times. concentration in 1.times.SB17, 0.05% Tween-20 with
appropriate photo-cleavable, biotinylated primers, where the
resultant primer concentration was 3 times the relevant aptamer
concentration. The primers hybridized to all or part of the
corresponding aptamer.
Each of the 3, 8.times. aptamer solutions were diluted separately
1:4 into 1.times.SB17, 0.05% Tween-20 (1500 .mu.L of 8.times. stock
into 4500 .mu.L of 1.times.SB17, 0.05% Tween-20) to achieve a
2.times. concentration. Each diluted aptamer master mix was then
split, 1500 .mu.L each, into 4, 2 mL screw cap tubes and brought to
95.degree. C. for 5 minutes, followed by a 37.degree. C. incubation
for 15 minutes. After incubation, the 4, 2 mL tubes corresponding
to a particular aptamer master mix were combined into a reagent
trough, and 55 .mu.L of a 2.times. aptamer mix (for all three
mixes) was manually pipetted into a 96-well Hybaid plate and the
plate foil sealed. The final result was 3, 96-well, foil-sealed
Hybaid plates. The individual aptamer concentration ranged from
0.5-4 nM as indicated in Table 2.
2. Assay Sample Preparation
Frozen aliquots of 100% serum, stored at -80.degree. C., were
placed in 25.degree. C. water bath for 10 minutes. Thawed samples
were placed on ice, gently vortexed (set on 4) for 8 seconds and
then replaced on ice.
A 20% sample solution was prepared by transferring 16 .mu.L of
sample using a 50 .mu.L 8-channel spanning pipettor into 96-well
Hybaid plates, each well containing 64 .mu.L of the appropriate
sample diluent at 4.degree. C. (0.8.times.SB17, 0.05% Tween-20, 2
.mu.M Z-block_2, 0.6 mM MgCl.sub.2 for serum). This plate was
stored on ice until the next sample dilution steps were
initiated.
To commence sample and aptamer equilibration, the 20% sample plate
was briefly centrifuged and placed on the Beckman FX where it was
mixed by pipetting up and down with the 96-well pipettor. A 2%
sample was then prepared by diluting 10 .mu.L of the 20% sample
into 90 .mu.L of 1.times.SB17, 0.05% Tween-20. Next, dilution of 6
.mu.L of the resultant 2% sample into 194 .mu.L of 1.times.SB17,
0.05% Tween-20 made a 0.06% sample plate. Dilutions were done on
the Beckman Biomek Fx.sup.P. After each transfer, the solutions
were mixed by pipetting up and down. The 3 sample dilution plates
were then transferred to their respective aptamer solutions by
adding 55 .mu.L of the sample to 55 .mu.L of the appropriate
2.times. aptamer mix. The sample and aptamer solutions were mixed
on the robot by pipetting up and down.
3. Sample Equilibration Binding
The sample/aptamer plates were foil sealed and placed into a
37.degree. C. incubator for 3.5 hours before proceeding to the
Catch 1 step.
4. Preparation of Catch 2 bead plate
An 11 mL aliquot of MyOne (Invitrogen Corp., Carlsbad, Calif.)
Streptavidin C1 beads was washed 2 times with equal volumes of 20
mM NaOH (5 minute incubation for each wash), 3 times with equal
volumes of 1.times.SB17, 0.05% Tween-20 and resuspended in 11 mL
1.times.SB17, 0.05% Tween-20. Using a 12-span multichannel
pipettor, 50 .mu.L of this solution was manually pipetted into each
well of a 96-well Hybaid plate. The plate was then covered with
foil and stored at 4.degree. C. for use in the assay.
5. Preparation of Catch 1 bead plates
Three 0.45 .mu.m Millipore HV plates (Durapore membrane, Cat #
MAHVN4550) were equilibrated with 100 .mu.L of 1.times.SB17, 0.05%
Tween-20 for at least 10 minutes. The equilibration buffer was then
filtered through the plate and 133.3 .mu.L of a 7.5%
Streptavidin-agarose bead slurry (in 1.times.SB17, 0.05% Tween-20)
was added into each well. To keep the streptavidin-agarose beads
suspended while transferring them into the filter plate, the bead
solution was manually mixed with a 200 .mu.L, 12-channel pipettor,
15 times. After the beads were distributed across the 3 filter
plates, a vacuum was applied to remove the bead supernatant.
Finally, the beads were washed in the filter plates with 200 .mu.L
1.times.SB17, 0.05% Tween-20 and then resuspended in 200 .mu.L
1.times.SB17, 0.05% Tween-20. The bottoms of the filter plates were
blotted and the plates stored for use in the assay.
6. Loading the Cytomat
The cytomat was loaded with all tips, plates, all reagents in
troughs (except NHS-biotin reagent which was prepared fresh right
before addition to the plates), 3 prepared catch 1 filter plates
and 1 prepared MyOne plate.
7. Catch 1
After a 3.5 hour equilibration time, the sample/aptamer plates were
removed from the incubator, centrifuged for about 1 minute, foil
removed, and placed on the deck of the Beckman Biomek Fx.sup.P. The
Beckman Biomek Fx.sup.P program was initiated. All subsequent steps
in Catch 1 were performed by the Beckman Biomek Fx.sup.P robot
unless otherwise noted. Within the program, the vacuum was applied
to the Catch 1 filter plates to remove the bead supernatant. One
hundred microlitres of each of the 10%, 1% and 0.03% equilibration
binding reactions were added to their respective Catch 1 filtration
plates, and each plate was mixed using an on-deck orbital shaker at
800 rpm for 10 minutes.
Unbound solution was removed via vacuum filtration. The catch 1
beads were washed with 190 .mu.L of 100 .mu.M biotin in
1.times.SB17, 0.05% Tween-20 followed by 190 .mu.L of 1.times.SB17,
0.05% Tween-20 by dispensing the solution and immediately drawing a
vacuum to filter the solution through the plate.
Next, 190 .mu.L 1.times.SB17, 0.05% Tween-20 was added to the Catch
1 plates. Plates were blotted to remove droplets using an on-deck
blot station and then incubated with orbital shakers at 800 rpm for
10 minutes at 25.degree. C.
The robot removed this wash via vacuum filtration and blotted the
bottom of the filter plate to remove droplets using the on-deck
blot station.
8. Tagging
A NHS-PEO4-biotin aliquot was thawed at 37.degree. C. for 6 minutes
and then diluted 1:100 with tagging buffer (SB17 at pH=7.25 0.05%
Tween-20). The NHS-PEO4-biotin reagent was dissolved at 100 mM
concentration in anhydrous DMSO and had been stored frozen at
-20.degree. C. Upon a robot prompt, the diluted NHS-PEO4-biotin
reagent was manually added to an on-deck trough and the robot
program was manually re-initiated to dispense 100 .mu.L of the
NHS-PEO4-biotin into each well of each Catch 1 filter plate. This
solution was allowed to incubate with Catch 1 beads shaking at 800
rpm for 5 minutes on the orbital shakers.
9. Kinetic Challenge and Photo-Cleavage
The tagging reaction was quenched by the addition of 150 .mu.L of
20 mM glycine in 1.times.SB17, 0.05% Tween-20 to the Catch 1 plates
while still containing the NHS tag. The plates were then incubated
for 1 minute on orbital shakers at 800 rpm. The NHS-tag/glycine
solution was removed via vacuum filtration. Next, 190 .mu.L 20 mM
glycine (1.times.SB17, 0.05% Tween-20) was added to each plate and
incubated for 1 minute on orbital shakers at 800 rpm before removal
by vacuum filtration.
190 .mu.L of 1.times.SB17, 0.05% Tween-20 was added to each plate
and removed by vacuum filtration.
The wells of the Catch 1 plates were subsequently washed three
times by adding 190 .mu.L 1.times.SB17, 0.05% Tween-20, placing the
plates on orbital shakers for 1 minute at 800 rpm followed by
vacuum filtration. After the last wash the plates were placed on
top of a 1 mL deep-well plate and removed from the deck. The Catch
1 plates were centrifuged at 1000 rpm for 1 minute to remove as
much extraneous volume from the agarose beads before elution as
possible.
The plates were placed back onto the Beckman Biomek Fx.sup.P and 85
.mu.L of 10 mM DxSO4 in 1.times.SB17, 0.05% Tween-20 was added to
each well of the filter plates.
The filter plates were removed from the deck, placed onto a
Variomag Thermoshaker (Thermo Fisher Scientific, Inc., Waltham,
Mass.) under the BlackRay (Ted Pella, Inc., Redding, Calif.) light
sources, and irradiated for 10 minutes while shaking at 800
rpm.
The photocleaved solutions were sequentially eluted from each Catch
1 plate into a common deep well plate by first placing the 10%
Catch 1 filter plate on top of a 1 mL deep-well plate and
centrifuging at 1000 rpm for 1 minute. The 1% and 0.03% catch 1
plates were then sequentially centrifuged into the same deep well
plate.
10. Catch 2 Bead Capture
The 1 mL deep well block containing the combined eluates of catch 1
was placed on the deck of the Beckman Biomek Fx.sup.P for catch
2.
The robot transferred all of the photo-cleaved eluate from the 1 mL
deep-well plate onto the Hybaid plate containing the previously
prepared catch 2 MyOne magnetic beads (after removal of the MyOne
buffer via magnetic separation).
The solution was incubated while shaking at 1350 rpm for 5 minutes
at 25.degree. C. on a Variomag Thermoshaker (Thermo Fisher
Scientific, Inc., Waltham, Mass.).
The robot transferred the plate to the on deck magnetic separator
station. The plate was incubated on the magnet for 90 seconds
before removal and discarding of the supernatant.
11. 37.degree. C. 30% Glycerol Washes
The catch 2 plate was moved to the on-deck thermal shaker and 75
.mu.L of 1.times.SB17, 0.05% Tween-20 was transferred to each well.
The plate was mixed for 1 minute at 1350 rpm and 37.degree. C. to
resuspend and warm the beads. To each well of the catch 2 plate, 75
.mu.L of 60% glycerol at 37.degree. C. was transferred and the
plate continued to mix for another minute at 1350 rpm and
37.degree. C. The robot transferred the plate to the 37.degree. C.
magnetic separator where it was incubated on the magnet for 2
minutes and then the robot removed and discarded the supernatant.
These washes were repeated two more times.
After removal of the third 30% glycerol wash from the catch 2
beads, 150 .mu.L of 1.times.SB17, 0.05% Tween-20 was added to each
well and incubated at 37.degree. C., shaking at 1350 rpm for 1
minute, before removal by magnetic separation on the 37.degree. C.
magnet.
The catch 2 beads were washed a final time using 150 .mu.L
1.times.SB19, 0.05% Tween-20 with incubation for 1 minute while
shaking at 1350 rpm, prior to magnetic separation.
12. Catch 2 Bead Elution and Neutralization
The aptamers were eluted from catch 2 beads by adding 105 .mu.L of
100 mM CAPSO with 1 M NaCl, 0.05% Tween-20 to each well. The beads
were incubated with this solution with shaking at 1300 rpm for 5
minutes.
The catch 2 plate was then placed onto the magnetic separator for
90 seconds prior to transferring 90 .mu.L of the eluate to a new
96-well plate containing 10 .mu.L of 500 mM HCl, 500 mM HEPES,
0.05% Tween-20 in each well. After transfer, the solution was mixed
robotically by pipetting 90 .mu.L up and down five times.
13. Hybridization
The Beckman Biomek Fx.sup.P transferred 20 .mu.L of the neutralized
catch 2 eluate to a fresh Hybaid plate, and 5 .mu.L of 10.times.
Agilent Block, containing a 10.times. spike of hybridization
controls, was added to each well. Next, 25 .mu.L of 2.times.
Agilent Hybridization buffer was manually pipetted to the each well
of the plate containing the neutralized samples and blocking buffer
and the solution was mixed by manually pipetting 25 .mu.L up and
down 15 times slowly to avoid extensive bubble formation. The plate
was spun at 1000 rpm for 1 minute.
A gasket slide was placed into an Agilent hybridization chamber and
40 .mu.L of each of the samples containing hybridization and
blocking solution was manually pipetted into each gasket. An
8-channel variable spanning pipettor was used in a manner intended
to minimize bubble formation. Custom Agilent microarray slides
(Agilent Technologies, Inc., Santa Clara, Calif.), with their
Number Barcode facing up, were then slowly lowered onto the gasket
slides (see Agilent manual for detailed description).
The top of the hybridization chambers were placed onto the
slide/backing sandwich and clamping brackets slid over the whole
assembly. These assemblies were tightly clamped by turning the
screws securely.
Each slide/backing slide sandwich was visually inspected to assure
the solution bubble could move freely within the sample. If the
bubble did not move freely the hybridization chamber assembly was
gently tapped to disengage bubbles lodged near the gasket.
The assembled hybridization chambers were incubated in an Agilent
hybridization oven for 19 hours at 60.degree. C. rotating at 20
rpm.
14. Post Hybridization Washing
Approximately 400 mL Agilent Wash Buffer 1 was placed into each of
two separate glass staining dishes. One of the staining dishes was
placed on a magnetic stir plate and a slide rack and stir bar were
placed into the buffer.
A staining dish for Agilent Wash 2 was prepared by placing a stir
bar into an empty glass staining dish.
A fourth glass staining dish was set aside for the final
acetonitrile wash.
Each of six hybridization chambers was disassembled. One-by-one,
the slide/backing sandwich was removed from its hybridization
chamber and submerged into the staining dish containing Wash 1. The
slide/backing sandwich was pried apart using a pair of tweezers,
while still submerging the microarray slide. The slide was quickly
transferred into the slide rack in the Wash 1 staining dish on the
magnetic stir plate.
The slide rack was gently raised and lowered 5 times. The magnetic
stirrer was turned on at a low setting and the slides incubated for
5 minutes.
When one minute was remaining for Wash 1, Wash Buffer 2 pre-warmed
to 37.degree. C. in an incubator was added to the second prepared
staining dish. The slide rack was quickly transferred to Wash
Buffer 2 and any excess buffer on the bottom of the rack was
removed by scraping it on the top of the stain dish. The slide rack
was gently raised and lowered 5 times. The magnetic stirrer was
turned on at a low setting and the slides incubated for 5
minutes.
The slide rack was slowly pulled out of Wash 2, taking
approximately 15 seconds to remove the slides from the
solution.
With one minute remaining in Wash 2 acetonitrile (ACN) was added to
the fourth staining dish. The slide rack was transferred to the
acetonitrile stain dish. The slide rack was gently raised and
lowered 5 times. The magnetic stirrer was turned on at a low
setting and the slides incubated for 5 minutes.
The slide rack was slowly pulled out of the ACN stain dish and
placed on an absorbent towel. The bottom edges of the slides were
quickly dried and the slide was placed into a clean slide box.
15. Microarray Imaging
The microarray slides were placed into Agilent scanner slide
holders and loaded into the Agilent Microarray scanner according to
the manufacturer's instructions.
The slides were imaged in the Cy3-channel at 5 .mu.m resolution at
the 100% PMT setting and the XRD option enabled at 0.05. The
resulting tiff images were processed using Agilent feature
extraction software version 10.5.
Example 2. Biomarker Identification
The identification of potential lung cancer biomarkers was
performed for three different diagnostic applications, diagnosis of
suspicious nodules from a CT scan, screening of asymptomatic
smokers for lung cancer, and diagnosing an individual with lung
cancer. Serum samples were collected from four different sites in
support of these three applications and include 48 NSCLC cases, 218
high risk controls composed of heavy smokers and patients with
benign nodules. The multiplexed aptamer affinity assay as described
in Example 1 was used to measure and report the RFU value for 820
analytes in each of these 264 samples. The KS-test was then applied
to each analyte. The KS-distance (Kolmogorov-Smirnov statistic)
between values from two sets of samples is a non parametric
measurement of the extent to which the empirical distribution of
the values from one set (Set A) differs from the distribution of
values from the other set (Set B). For any value of a threshold T
some proportion of the values from Set A will be less than T, and
some proportion of the values from Set B will be less than T. The
KS-distance measures the maximum (unsigned) difference between the
proportion of the values from the two sets for any choice of T.
Sets of biomarkers can be used to build classifiers that assign
samples to either a control or disease group. In fact, many such
classifiers were produced from these sets of biomarkers and the
frequency with which any biomarker was used in good scoring
classifiers determined. Those biomarkers that occurred most
frequently among the top scoring classifiers were the most useful
for creating a diagnostic test. In this example, Bayesian
classifiers were used to explore the classification space but many
other supervised learning techniques may be employed for this
purpose. The scoring fitness of any individual classifier was
gauged using the area under the receiver operating characteristic
curve (AUC of ROC) of the classifier at the Bayesian surface
assuming a disease prevalence of 0.5. This scoring metric varies
from zero to one, with one being an error-free classifier. The
details of constructing a Bayesian classifier from biomarker
population measurements are described in Example 3.
Example 3. Naive Bayesian Classification for Lung Cancer
From the list of biomarkers identified as useful for discriminating
between NSCLC and the high risk control group, a panel of five
biomarkers was selected and a naive Bayes classifier was
constructed, see Table 14. The class-dependent probability density
functions (pdfs), p(x.sub.i|c) and p(x.sub.i|d), where x.sub.i is
the log of the measured RFU value for biomarker i, and c and d
refer to the control and disease populations, were modeled as
normal distribution functions characterized by a mean .mu. and
variance .sigma..sup.2. The parameters for pdfs of the five
biomarkers are listed in Table 15 and an example of the raw data
along with the model fit to a normal pdf is displayed in FIG. 5.
The underlying assumption appears to fit the data quite well as
evidenced by FIG. 5.
The naive Bayes classification for such a model is given by the
following equation, where P(d) is the prevalence of the disease in
the population
.times..times..function..function..times..times..times..sigma..sigma..fun-
ction..mu..sigma..mu..sigma..times..times..function..function.
##EQU00012## appropriate to the test and n=5 here. Each of the
terms in the summation is a log-likelihood ratio for an individual
marker and the total log-likelihood ratio of a sample {tilde under
(x)} being free from the disease of interest (i.e. in this case,
NSCLC) versus having the disease is simply the sum of these
individual terms plus a term that accounts for the prevalence of
the disease. For simplicity, we assume P(d)=0.5 so that
.times..times..function..function. ##EQU00013##
Given an unknown sample measurement in log(RFU) for each of the ten
biomarkers of. The individual components comprising the log
likelihood ratio for control versus disease class are tabulated and
can be computed from the parameters in Table 15 and the values of
{tilde under (x)}. The sum of the individual log likelihood ratios
is 3.47, or a likelihood of being free from the disease versus
having the disease of 32:1, where likelihood=e.sup.3.47=32. All
five biomarkers are all consistently found to favor the control
group. Multiplying the likelihoods together gives the same results
as that shown above; a likelihood of 32:1 that the unknown sample
is free from the disease. In fact, this sample came from the
control population in the training set. Although this example
demonstrates the classification of serum samples using the
biomarkers in Table 15, the same approach can be used in any tissue
type with any set of biomarkers from Table 21.
Example 4. Greedy Algorithm for Selecting Biomarker Panels for
Classifiers Part 1
This example describes the selection of biomarkers from Table 21 to
form panels that can be used as classifiers in any of the methods
described herein. Panels of biomarkers containing MMP-12 and
Subsets of the biomarkers in Table 21 were selected to construct
classifiers with good performance. This method was also used to
determine which potential markers were included as biomarkers in
Example 2.
The measure of classifier performance used here is the area under
the ROC curve (AUC); a performance of 0.5 is the baseline
expectation for a random (coin toss) classifier, a classifier worse
than random would score between 0.0 and 0.5, a classifier with
better than random performance would score between 0.5 and 1.0. A
perfect classifier with no errors would have a sensitivity of 1.0,
a specificity of 1.0 and an AUC of 1.0. One can apply the methods
described in Example 4 to other common measures of performance such
as the F-measure, the sum of sensitivity and specificity, or the
product of sensitivity and specificity. Specifically one might want
to treat specificity and specificity with differing weight, so as
to select those classifiers which perform with higher specificity
at the expense of some sensitivity, or to select those classifiers
which perform with higher sensitivity at the expense of some
specificity. Since the method described here only involves a
measure of "performance", any weighting scheme which results in a
single performance measure can be used. Different applications will
have different benefits for true positive and true negative
findings, and also different costs associated with false positive
findings from false negative findings. For example, screening
asymptomatic smokers and the differential diagnosis of benign
nodules found on CT will not in general have the same optimal
trade-off between specificity and sensitivity. The different
demands of the two tests will in general require setting different
weighting to positive and negative misclassifications, reflected in
the performance measure. Changing the performance measure will in
general change the exact subset of markers selected from Table 21
for a given set of data.
For the Bayesian approach to the discrimination of lung cancer
samples from control samples described in Example 3, the classifier
was completely parameterized by the distributions of biomarkers in
the disease and benign training samples, and the list of biomarkers
was chosen from Table 21; that is to say, the subset of markers
chosen for inclusion determined a classifier in a one-to-one manner
given a set of training data.
The greedy method employed here was used to search for the optimal
subset of markers from Table 21. For small numbers of markers or
classifiers with relatively few markers, every possible subset of
markers was enumerated and evaluated in terms of the performance of
the classifier constructed with that particular set of markers (see
Example 4, Part 2). (This approach is well known in the field of
statistics as "best subset selection"; see, e.g., The Elements of
Statistical Learning-Data Mining, Inference, and Prediction, T.
Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd
edition, 2009). However, for the classifiers described herein, the
number of combinations of multiple markers can be very large, and
it was not feasible to evaluate every possible set of five markers,
for example, from the list of 86 markers (Table 21) (i.e.,
34,826,302 combinations). Because of the impracticality of
searching through every subset of markers, the single optimal
subset may not be found; however, by using this approach, many
excellent subsets were found, and, in many cases, any of these
subsets may represent an optimal one.
Instead of evaluating every possible set of markers, a "greedy"
forward stepwise approach may be followed (see, e.g., Dabney A R,
Storey J D (2007) Optimality Driven Nearest Centroid Classification
from Genomic Data. PLoS ONE 2(10): e1002.
doi:10.1371/journal.pone.0001002). Using this method, a classifier
is started with the best single marker (based on KS-distance for
the individual markers) and is grown at each step by trying, in
turn, each member of a marker list that is not currently a member
of the set of markers in the classifier. The one marker which
scores best in combination with the existing classifier is added to
the classifier. This is repeated until no further improvement in
performance is achieved. Unfortunately, this approach may miss
valuable combinations of markers for which some of the individual
markers are not all chosen before the process stops.
The greedy procedure used here was an elaboration of the preceding
forward stepwise approach, in that, to broaden the search, rather
than keeping just a single candidate classifier (marker subset) at
each step, a list of candidate classifiers was kept. The list was
seeded with every single marker subset (using every marker in the
table on its own). The list was expanded in steps by deriving new
classifiers (marker subsets) from the ones currently on the list
and adding them to the list. Each marker subset currently on the
list was extended by adding any marker from Table 1 not already
part of that classifier, and which would not, on its addition to
the subset, duplicate an existing subset (these are termed
"permissible markers"). Every existing marker subset was extended
by every permissible marker from the list. Clearly, such a process
would eventually generate every possible subset, and the list would
run out of space. Therefore, all the generated classifiers were
kept only while the list was less than some predetermined size
(often enough to hold all three marker subsets). Once the list
reached the predetermined size limit, it became elitist; that is,
only those classifiers which showed a certain level of performance
were kept on the list, and the others fell off the end of the list
and were lost. This was achieved by keeping the list sorted in
order of classifier performance; new classifiers which were at
least as good as the worst classifier currently on the list were
inserted, forcing the expulsion of the current bottom
underachiever. One further implementation detail is that the list
was completely replaced on each generational step; therefore, every
classifier on the list had the same number of markers, and at each
step the number of markers per classifier grew by one.
Since this method produced a list of candidate classifiers using
different combinations of markers, one may ask if the classifiers
can be combined in order to avoid errors which might be made by the
best single classifier, or by minority groups of the best
classifiers. Such "ensemble" and "committee of experts" methods are
well known in the fields of statistical and machine learning and
include, for example, "averaging", "voting", "stacking", "bagging"
and "boosting" (see, e.g., The Elements of Statistical
Learning--Data Mining, Inference, and Prediction, T. Hastie, et
al., editors, Springer Science+Business Media, LLC, 2nd edition,
2009). These combinations of simple classifiers provide a method
for reducing the variance in the classifications due to noise in
any particular set of markers by including several different
classifiers and therefore information from a larger set of the
markers from the biomarker table, effectively averaging between the
classifiers. An example of the usefulness of this approach is that
it can prevent outliers in a single marker from adversely affecting
the classification of a single sample. The requirement to measure a
larger number of signals may be impractical in conventional "one
marker at a time" antibody assays but has no downside for a fully
multiplexed aptamer assay. Techniques such as these benefit from a
more extensive table of biomarkers and use the multiple sources of
information concerning the disease processes to provide a more
robust classification.
Part 2
The biomarkers selected in Table 1 gave rise to classifiers which
perform better than classifiers built with "non-markers" (i.e.,
proteins having signals that did not meet the criteria for
inclusion in Table 1 (as described in Example 2)).
For classifiers containing only one, two, and three markers, all
possible classifiers obtained using the biomarkers in Table 1 were
enumerated and examined for the distribution of performance
compared to classifiers built from a similar table of randomly
selected non-markers signals.
In FIG. 17 and FIG. 18, the sum of the sensitivity and specificity
was used as the measure of performance; a performance of 1.0 is the
baseline expectation for a random (coin toss) classifier. The
histogram of classifier performance was compared with the histogram
of performance from a similar exhaustive enumeration of classifiers
built from a "non-marker" table of 40 non-marker signals; the 40
signals were randomly chosen from 400 aptamers that did not
demonstrate differential signaling between control and disease
populations (KS-distance <1.4).
FIG. 17 shows histograms of the performance of all possible one,
two, and three-marker classifiers built from the biomarker
parameters in Table 13 for biomarkers that can discriminate between
benign nodules and NSCLC and compares these classifiers with all
possible one, two, and three-marker classifiers built using the 40
"non-marker" aptamer RFU signals. FIG. 17A shows the histograms of
single marker classifier performance, FIG. 17B shows the histogram
of two marker classifier performance, and FIG. 17C shows the
histogram of three marker classifier performance.
In FIG. 17, the solid lines represent the histograms of the
classifier performance of all one, two, and three-marker
classifiers using the biomarker data for benign nodules and NSCLC
in Table 13. The dotted lines are the histograms of the classifier
performance of all one, two, and three-marker classifiers using the
data for benign nodules and NSCLC but using the set of random
non-marker signals.
FIG. 18 shows histograms of the performance of all possible one,
two, and three-marker classifiers built from the biomarker
parameters in Table 12 for biomarkers that can discriminate between
asymptomatic smokers and NSCLC and compares these with all possible
one, two, and three-marker classifiers built using 40 "non-marker"
aptamer RFU signals. FIG. 18A shows the histograms of single marker
classifier performance, FIG. 18B shows the histogram of two marker
classifier performance, and FIG. 18C shows the histogram of three
marker classifier performance.
In FIG. 18, the solid lines represent the histograms of the
classifier performance of all one, two, and three-marker
classifiers using the biomarker parameters for asymptomatic smokers
and NSCLC in Table 12. The dotted lines are the histograms of the
classifier performance of all one, two, and three-marker
classifiers using the data for asymptomatic smokers and NSCLC but
using the set of random non-marker signals.
The classifiers built from the markers listed in Table 1 form a
distinct histogram, well separated from the classifiers built with
signals from the "non-markers" for all one-marker, two-marker, and
three-marker comparisons. The performance and AUC score of the
classifiers built from the biomarkers in Table 1 also increase
faster with the number of markers than do the classifiers built
from the non-markers, the separation increases between the marker
and non-marker classifiers as the number of markers per classifier
increases. All classifiers built using the biomarkers listed in
Tables 38 and 39 perform distinctly better than classifiers built
using the "non-markers".
Part 3
To test whether a core subset of markers accounted for the good
performance of the classifiers, half of the markers were randomly
dropped from the lists of biomarkers in Tables 38 and 39. The
performance, as measured by sensitivity plus specificity, of
classifiers for distinguishing benign nodules from malignant
nodules dropped slightly by 0.07 (from 1.74 to 1.67), and the
performance of classifiers for distinguishing smokers who had
cancer from those who did not also dropped slightly by 0.06 (from
1.76 to 1.70). The implication of the performance characteristics
of subsets of the biomarker table is that multiple subsets of the
listed biomarkers are effective in building a diagnostic test, and
no particular core subset of markers dictates classifier
performance.
In the light of these results, classifiers that excluded the best
markers from Tables 12 and 13 were tested. FIG. 19 compares the
performance of classifiers built with the full list of biomarkers
in Tables 12 and 13 with the performance of classifiers built with
a set of biomarkers from Tables 38 and 39 excluding top ranked
markers.
FIG. 19 demonstrates that classifiers constructed without the best
markers perform well, implying that the performance of the
classifiers was not due to some small core group of markers and
that the changes in the underlying processes associated with
disease are reflected in the activities of many proteins. Many
subsets of the biomarkers in Table 1 performed close to optimally,
even after removing the top 15 of the 40 markers from Table 1.
FIG. 19A shows the effect on classifiers for discriminating benign
nodules from NSCLC built with 2 to 10 markers. Even after dropping
the 15 top-ranked markers (ranked by KS-distance) from Table 13,
the benign nodule vs. NSCLC performance increased with the number
of markers selected from the table to reach over 1.65
(Sensitivity+Specificity).
FIG. 19B shows the effect on classifiers for discriminating
asymptomatic smokers from NSCLC built with 2 to 10 markers. Even
after dropping the 15 top-ranked markers (ranked by KS-distance)
from Table 12, the asymptomatic smokers vs. NSCLC performance
increased with the number of markers selected from the table to
reach over 1.7 (Sensitivity+Specificity), and closely approached
the performance of the best classifier selected from the full list
of biomarkers in Table 12.
Finally, FIG. 20 shows how the ROC performance of typical
classifiers constructed from the list of parameters in Tables 12
and 13 according to Example 3. FIG. 20A shows the model performance
from assuming the independence of markers as in Example 3, and FIG.
20B shows the actual ROC curves using the assay data set used to
generate the parameters in Tables 12 and 13. It can be seen that
the performance for a given number of selected markers was
qualitatively in agreement, and that quantitative agreement
degraded as the number of markers increases. (This is consistent
with the notion that the information contributed by any particular
biomarker concerning the disease processes is redundant with the
information contributed by other biomarkers provided in Tables 12
and 13). FIG. 20 thus demonstrates that Tables 12 and 13 in
combination with the methods described in Example 3 enable the
construction and evaluation of a great many classifiers useful for
the discrimination of NSCLC from benign nodules and the
discrimination of asymptomatic smokers who have NSCLC from those
who do not have NSCLC.
Example 5. Aptamer Specificity Demonstration in a Pull-Down
Assay
The final readout on the multiplex assay is based on the amount of
aptamer recovered after the successive capture steps in the assay.
The multiplex assay is based on the premise that the amount of
aptamer recovered at the end of the assay is proportional to the
amount of protein in the original complex mixture (e.g., plasma).
In order to demonstrate that this signal is indeed derived from the
intended analyte rather than from non-specifically bound proteins
in plasma, we developed a gel-based pull-down assay in plasma. This
assay can be used to visually demonstrate that a desired protein is
in fact pulled out from plasma after equilibration with an aptamer
as well as to demonstrate that aptamers bound to their intended
protein targets can survive as a complex through the kinetic
challenge steps in the assay. In the experiments described in this
example, recovery of protein at the end of this pull-down assay
requires that the protein remain non-covalently bound to the
aptamer for nearly two hours after equilibration. Importantly, in
this example we also provide evidence that non-specifically bound
proteins dissociate during these steps and do not contribute
significantly to the final signal. It should be noted that the
pull-down procedure described in this example includes all of the
key steps in the multiplex assay described above.
Plasma Pull-down Assay
Plasma samples were prepared by diluting 50 .mu.L EDTA-plasma to
100 .mu.L in SB18 with 0.05% Tween-20 (SB18T) and 2 .mu.M Z-Block.
The plasma solution was equilibrated with 10 pmoles of a
PBDC-aptamer in a final volume of 150 .mu.L for 2 hours at
37.degree. C. After equilibration, complexes and unbound aptamer
were captured with 133 .mu.L of a 7.5% Streptavidin-agarose bead
slurry by incubating with shaking for 5 minutes at RT in a Durapore
filter plate. The samples bound to beads were washed with biotin
and with buffer under vacuum as described in Example 1. After
washing, bound proteins were labeled with 0.5 mM NHS-S-S-biotin,
0.25 mM NHS-Alexa647 in the biotin diluent for 5 minutes with
shaking at RT. This staining step allows biotinylation for capture
of protein on streptavidin beads as well as highly sensitive
staining for detection on a gel. The samples were washed with
glycine and with buffer as described in Example 1. Aptamers were
released from the beads by photocleavage using a Black Ray light
source for 10 minutes with shaking at RT. At this point, the
biotinylated proteins were captured on 0.5 mg MyOne Streptavidin
beads by shaking for 5 minutes at RT. This step will capture
proteins bound to aptamers as well as proteins that may have
dissociated from aptamers since the initial equilibration. The
beads were washed as described in Example 1. Proteins were eluted
from the MyOne Streptavidin beads by incubating with 50 mM DTT in
SB17T for 25 minutes at 37.degree. C. with shaking. The eluate was
then transferred to MyOne beads coated with a sequence
complimentary to the 3' fixed region of the aptamer and incubated
for 25 minutes at 37.degree. C. with shaking. This step captures
all of the remaining aptamer. The beads were washed 2.times. with
100 .mu.L SB17T for 1 minute and 1.times. with 100 .mu.L SB19T for
1 minute. Aptamer was eluted from these final beads by incubating
with 45 .mu.L 20 mM NaOH for 2 minutes with shaking to disrupt the
hybridized strands. 40 .mu.L of this eluate was neutralized with 10
.mu.L 80 mM HCl containing 0.05% Tween-20. Aliquots representing 5%
of the eluate from the first set of beads (representing all plasma
proteins bound to the aptamer) and 20% of the eluate from the final
set of beads (representing all plasma proteins remaining bound at
the end of our clinical assay) were run on a NuPAGE 4-12% Bis-Tris
gel (Invitrogen) under reducing and denaturing conditions. Gels
were imaged on an Alpha Innotech FluorChem Q scanner in the Cy5
channel to image the proteins.
Pull-down gels for aptamers were selected against LBP
(.about.1.times.10.sup.-7 M in plasma, polypeptide MW .about.60
kDa), C9 (.about.1.times.10.sup.-6 M in plasma, polypeptide MW
.about.60 kDa), and IgM (.about.9.times.10.sup.-6 M in plasma, MW
.about.70 kDa and 23 kDa), respectively. (See FIG. 16).
For each gel, lane 1 is the eluate from the Streptavidin-agarose
beads, lane 2 is the final eluate, and lane 3 is a MW marker lane
(major bands are at 110, 50, 30, 15, and 3.5 kDa from top to
bottom). It is evident from these gels that there is a small amount
non-specific binding of plasma proteins in the initial
equilibration, but only the target remains after performing the
capture steps of the assay. It is clear that the single aptamer
reagent is sufficient to capture its intended analyte with no
up-front depletion or fractionation of the plasma. The amount of
remaining aptamer after these steps is then proportional to the
amount of the analyte in the initial sample.
Example 6. Analysis of NSCLC Surgical Resections
To demonstrate the utility of the platform based technology
described herein to identify disease-related biomarkers from
tissues, homogenized tissues samples from surgical resections
obtained from eight NSCLC patients were analyzed, All NSCLC
patients were smokers, ranging in age from 47 to 75 years old and
covering NSCLC stages 1A through 3B (Table 17). All tissue samples
were obtained by freezing the tissue within 5-10 minutes of
excision during surgery and after placing the tissues in OCT medium
(10.24% polyvinyl alcohol, 4.26% polyethylene glycol, and 85.5%
non-reactive ingredients). Three samples were obtained from each
resection: tumor tissue sample, adjacent healthy tissue (within 1
cm of the tumor) and distant uninvolved lung tissue. While keeping
the samples constantly frozen, five 10 inn thick sections were cut,
trimmed of excess OCT from around the tissue, and placed into
frozen 1.5 mL microfuge tubes. Following the addition of 200 .mu.L
homogenization buffer (SB18 buffer plus PI cocktail (Pierce HALT
protease inhibitor cocktail without magnesium), the samples were
homogenized in the microfuge tubes on ice with rotary pestle for 30
seconds, until no tissue fragments were visible. The samples were
then spun in a centrifuge at 21,000 g for 10 minutes and filtered
through a 0.2 .mu.m multiwell plate filter into a sterile multiwell
plate. Five .mu.L aliquots were taken for BCA protein assay and the
rest of the sample was stored frozen and sealed in 96 well plates
at -70.degree. C.
Sample total protein was adjusted to 16 .mu.g/mL in SB17T buffer
(SB17 buffer containing 0.05% tween 20) for proteomic profiling.
Samples prepared in this manner were run on the multiplex aptamer
assay which, as noted above, measures over 800 proteins as
described previously (Ostroff et al., "Unlocking biomarker
discovery: Large scale application of aptamer proteomic technology
for early detection of lung cancer," Nature Precedings, (2010)).
Among the measured analytes, most were unchanged between tumor,
adjacent tissue and distal tissue. However, some proteins were
clearly suppressed (FIG. 24) while others were elevated
substantially in tumor tissues (FIG. 23) compared to adjacent and
distal tissues.
The foregoing embodiments and examples are intended only as
examples. No particular embodiment, example, or element of a
particular embodiment or example is to be construed as a critical,
required, or essential element or feature of any of the claims.
Further, no element described herein is required for the practice
of the appended claims unless expressly described as "essential" or
"critical." Various alterations, modifications, substitutions, and
other variations can be made to the disclosed embodiments without
departing from the scope of the present application, which is
defined by the appended claims. The specification, including the
figures and examples, is to be regarded in an illustrative manner,
rather than a restrictive one, and all such modifications and
substitutions are intended to be included within the scope of the
application. Accordingly, the scope of the application should be
determined by the appended claims and their legal equivalents,
rather than by the examples given above. For example, steps recited
in any of the method or process claims may be executed in any
feasible order and are not limited to an order presented in any of
the embodiments, the examples, or the claims. Further, in any of
the aforementioned methods, one or more biomarkers of Table 18,
Table 20, or Table 21 can be specifically excluded either as an
individual biomarker or as a biomarker from any panel.
TABLE-US-00001 TABLE 1 Lung Cancer Biomarkers Column #4 Column #5
Gene Benign Column #6 Column #2 Designation Nodule Smokers Column
#1 Biomarker Column #3 (Entrez versus versus Biomarker #
Designation Alternate Protein Names Gene Link) NSCLC NSCLC 1 AMPM2
Methionine aminopeptidase 2 METAP2 X p67eIF2 p67 Initiation factor
2-associated 67 kDa glycoprotein Peptidase M 2 MetAP 2 MAP 2 2 Apo
A-I apolipoprotein A-I APOA1 X Apolipoprotein A-1 3 b-ECGF FGF
acidic FGF1 X FGF1 beta-ECGF Beta-endothelial cell growth factor 4
BLC BLC B lymphocyte chemoattractant CXCL13 X X Small inducible
cytokine B13 CXCL13 BCA-1 5 BMP-1 Bone morphogenetic protein 1 BMP1
X X Procollagen C-proteinase PCP Mammalian tolloid protein mTId 6
BTK Tyrosine-protein kinase BTK BTK X Bruton tyrosine kinase
Agammaglobulinaemia tyrosine kinase ATK B-cell progenitor kinase 7
C1s Complement C1s subcomponent C1S X C1s, Activated, Two-Chain
Form 8 C9 Complement component C9 C9 X X 9 Cadherin E Cadherin-1
CDH1 X Epithelial cadherin E-cadherin Uvomorulin CAM 120/80
CD_antigen = CD324 10 Cadherin-6 Kidney-cadherin CDH6 X K-cadherin
11 Calpain I Calpain I (dimer of Calpain-1 CAPN1 X catalytic
subunit and Calpain small CAPNS1 subunit 1) synonyms of the
catalytic subunit include Calpain-1 large subunit:
Calcium-activated neutral proteinase 1 Micromolar-calpain Cell
proliferation-inducing gene 30 protein synonyms of the small
subunit include: Calcium-dependent protease small subunit 1
Calcium-activated neutral proteinase small subunit CANP small
subunit 12 Catalase Catalase CAT X 13 CATC Dipeptidyl-peptidase 1
precursor CTSC X Dipeptidyl-peptidase I DPP-I DPPI Cathepsin C
Cathepsin J Dipeptidyl transferase 14 Cathepsin H Cathepsin H CTSH
X 15 CD30 Ligand Tumor necrosis factor ligand TNFSF8 X X
superfamily member 8 CD30-L CD153 antigen 16 CDK5-p35 CDK5/p35 is a
dimer of Cell division CDK5 X protein kinase 5, and the p35 chain
CDK5R1 of Cyclin-dependent kinase 5 activator 1 Cell division
protein kinase 5 is also known as: Cyclin-dependent kinase 5 Tau
protein kinase II catalytic subunit Serine/threonine-protein kinase
PSSALRE p35 chain of Cyclin-dependent kinase 5 activator 1 is also
known as: Cyclin-dependent kinase 5 regulatory subunit 1 CDK5
activator 1 Cyclin-dependent kinase 5 regulatory subunit 1 Tau
protein kinase II regulatory subunit. 17 CK-MB Creatine
Phosphokinase-MB CKB X X Isoenzyme, which is a dimer of CKM
Creatine kinase M-type and B-type Creatine kinase M and B chains
M-CK and B-CK CKM and CKB 18 CNDP1 Beta-Ala-His dipeptidase CNDP1 X
X Carnosine dipeptidase 1 CNDP dipeptidase 1 Serum carnosinase
Glutamate carboxypeptidase-like protein 2 19 Contactin-5 Neural
recognition molecule NB-2 CNTN5 X hNB-2 20 CSK Tyrosine-protein
kinase CSK CSK X X C-SRC kinase Protein-tyrosine kinase CYL 21
Cyclophilin A Cyclophilin A PPIA X Peptidyl-prolyl cis-trans
isomerase A PPlase Peptidylprolyl isomerase Cyclosporin A-binding
protein Rotamase A PPlase A 22 Endostatin Endostatin, which is
cleaved from COL18A1 X Collagen alpha-1(XVIII) chain 23 ERBB1
Epidermal growth factor receptor EGFR X X Receptor tyrosine-protein
kinase ErbB-1 EGFR HER1 24 FGF-17 Fibroblast Growth Factor-17 FGF17
X X 25 FYN Proto-oncogene tyrosine-protein FYN X kinase Fyn
Protooncogene Syn p59-Fyn 26 GAPDH, liver Glyceraldehyde
3-phosphate GAPDH X X dehydrogenase 27 HMG-1 High mobility group
protein B1 HMGB1 X amphoterin Neurite growth-promoting protein 28
HSP 90a Heat shock protein HSP 90-alpha HSP90AA1 X X HSP 86 Renal
carcinoma antigen NY-REN- 38 29 HSP 90b Heat shock protein HSP
90-beta HSP90AB1 X HSP 90 HSP 84 30 IGFBP-2 Insulin-like growth
factor-binding IGFBP2 X X protein 2 (IGF-binding protein 2;
IGFBP-2; IBP-2; BP2) 31 IL-15 Ra Interleukin-15 receptor subunit
alpha IL15RA X 32 IL-17B Interleukin-17B IL17B X Neuronal
interleukin-17 related factor Interleukin-20 Cytokine-like protein
ZCYTO7 33 IMB1 Importin subunit beta-1 KPNB1 X Karyopherin subunit
beta-1 Nuclear factor P97 Importin-90 34 Kallikrein 7 Kallikrein-7
KLK7 X hK7 Stratum corneum chymotryptic enzyme hSCCE Serine
protease 6 35 KPCI Protein kinase C iota type PRKCI X X nPKC-iota
Atypical protein kinase C- lambda/iota aPKC-lambda/iota
PRKC-lambda/iota 36 LDH-H 1 L-lactate dehydrogenase B chain LDHB X
LDH-B LDH heart subunit LDH-H Renal carcinoma antigen NY-REN- 46 37
LGMN Legumain LGMN X Protease, cysteine 1 Asparaginyl endopeptidase
38 LRIG3 Leucine-rich repeats and LRIG3 X X immunoglobulin-like
domains protein 3 39 Macrophage Macrophage mannose receptor 1 MRC1
X mannose MMR receptor C-type lectin domain family 13 member D
CD_antigen = CD206 40 MEK1 Dual specificity mitogen-activated
MAP2K1 X X protein kinase kinase 1 MAPK/ERK kinase 1 ERK activator
kinase 1 41 METAP1 Methionine aminopeptidase 1 METAP1 X MetAP 1 MAP
1 Peptidase M1 42 Midkine Neurite outgrowth-promoting protein MDK X
Neurite outgrowth-promoting factor 2 Midgestation and kidney
protein Amphiregulin-associated protein ARAP 43 MIP-5 C-C motif
chemokine 15 MIP5 X Small-inducible cytokine A15 Macrophage
inflammatory protein 5 Chemokine CC-2 HCC-2 NCC-3 MIP-1 delta
Leukotactin-1 LKN-1 Mrp-2b 44 MK13 Mitogen-activated protein kinase
13 MAPK13 X MAP kinase p38 delta Mitogen-activated protein kinase
p38 delta Stress-activated protein kinase 4 45 MMP-7 Matrilysin
MMP7 X Pump-1 protease Uterine metalloproteinase Matrix
metalloproteinase-7 MMP-7 Matrin 46 NACA Nascent
polypeptide-associated NACA X complex subunit alpha NAC-alpha
Alpha-NAC Allergen = Hom s 2 47 NAGK N-acetylglucosamine kinase
NAGK X GlcNAc kinase 48 PARC C-C motif chemokine 18 CCL18 X
Small-inducible cytokine A18 Macrophage inflammatory protein 4
MIP-4 Pulmonary and activation-regulated chemokine CC chemokine
PARC Alternative macrophage activation- associated CC chemokine 1
AMAC-1 Dendritic cell chemokine 1 DC-CK1 49 Proteinase-3
Proteinase-3 PRTN3 X PR-3 AGP7 P29 Myeloblastin Leukocyte
proteinase 3 Wegener's autoantigen Neutrophil proteinase 4 NP4
C-ANCA antigen 50 Prothrombin Prothrombin F2 X X
(Coagulation factor II) 51 PTN Pleiotrophin PTN X Heparin-binding
growth-associated molecule HB-GAM Heparin-binding growth factor 8
HBGF-8 Osteoblast-specific factor 1 OSF-1 Heparin-binding neurite
outgrowth- promoting factor 1 HBNF-1 Heparin-binding brain mitogen
HBBM 52 RAC1 Ras-related C3 botulinum toxin RAC1 X substrate 1
p21-Rac1 Ras-like protein TC25 Cell migration-inducing gene 5
protein 53 Renin Renin REN X Angiotensinogenase 54 RGM-C
Hemojuvelin HFE2 X Hemochromatosis type 2 protein RGM domain family
member C 55 SCF sR Mast/stem cell growth factor KIT X X receptor
(SCFR; Proto-oncogene tyrosine- protein kinase Kit; c-kit;
CD_antigen = CD117) 56 sL-Selectin sL-Selectin SELL X Leukocyte
adhesion molecule-1 Lymph node homing receptor LAM-1 L-Selectin
L-Selectin, soluble Leukocyte surface antigen Leu-8 TQ1 gp90-MEL
Leukocyte-endothelial cell adhesion molecule 1 LECAM1 CD62
antigen-like family member L 57 TCTP Translationally-controlled
tumor TPT1 X protein p23 Histamine-releasing factor HRF Fortilin 58
UBE2N Ubiquitin-conjugating enzyme E2 N UBE2N X Ubiquitin-protein
ligase N Ubiquitin carrier protein N Ubc13 Bendless-like
ubiquitin-conjugating enzyme 59 Ubiquitin + 1 Ubiquitin RPS27A X 60
VEGF Vascular endothelial growth factor A VEGFA X VEGF-A Vascular
permeability factor 61 YES Proto-oncogene tyrosine-protein YES X
kinase Yes c-Yes p61-Yes
TABLE-US-00002 TABLE 2 Aptamer Concentrations Final Aptamer Target
Conc (nM) AMPM2 0.5 Apo A-I 0.25 b-ECGF 2 BLC 0.25 BMP-1 1 BTK 0.25
C1s 0.25 C9 1 Cadherin E 0.25 Cadherin-6 0.5 Calpain I 0.5 Catalase
0.5 CATC 0.5 Cathepsin H 0.5 CD30 Ligand 0.5 CDK5/p35 0.5 CK-MB 1
CNDP1 0.5 Contactin-5 1 CSK Cyclophilin A 0.5 Endostatin 1 ERBB1
0.5 FYN 0.25 GAPDH, liver 0.25 HMG-1 0.5 HSP 90a 0.5 HSP 90b 0.5
IGFBP-2 1 IL-15 Ra 0.5 IL-17B 0.5 IMB1 1 Kallikrein 7 0.5 KPCI 0.25
LDH-H 1 0.5 LGMN 0.5 LRIG3 0.25 Macrophage 2 mannose receptor MEK1
0.5 METAP1 0.25 Midkine 0.5 MIP-5 1 MK13 1 MMP-7 0.25 NACA 0.5 NAGK
0.5 PARC 0.5 Proteinase-3 1 Prothrombin 0.5 PTN 0.25 RAC1 0.5 Renin
0.25 RGM-C 0.5 SCF sR 1 sL-Selectin 0.5 TCTP 0.5 UBE2N 0.5
Ubiquitin + 1 0.5 VEGF 1 YES 0.5
TABLE-US-00003 TABLE 3 Benign Asymptomatic Site NSCLC Nodule
Smokers 1 32 0 47 2 63 176 128 3 70 195 94 4 54 49 83 Sum 213 420
352 Males 51% 46% 49% Females 49% 54% 51% Median 68 60 57 Age
Median 40 42 34 Pack Years Median 1.94 2.43 2.58 FEV1 Median 74 88
90 FEV 1% Median 70 72 73 FEV1/FVC
TABLE-US-00004 TABLE 4 Biomarkers Identified in Benign Nodule-NSCLC
in Aggregated Data SCF sR CNDP1 Stress-induced- phosphoprotein 1
RGM-C MEK1 LRIG3 ERBB1 MDHC ERK-1 Cadherin E Catalase Cyclophilin A
CK-MB BMP-1 Caspase-3 METAP1 ART UFM1 HSP90a C9 RAC1 IGFBP-2 TCPTP
Peroxiredoxin-1 Calpain I RPS6KA3 PAFAHbeta subunit KPCI IMB1 MK01
MMP-7 UBC9 Integrina1b1 .beta.-ECGF Ubiquitin + 1 IDE HSP90b
Cathepsin H CAMK2A NAGK CSK21 BLC FGF-17 BTK BARK1 Macrophage
mannose Thrombin eIF-5 receptor MK13 LYN UFC1 NACA HSP70 RS7 GAPDH
UBE2N PRKACA CSK TCTP AMPM2 Activin A RabGDPdissociation
Stress-induced- inhibitor beta phosphoprotein 1 Prothrombin
MAPKAPK3
TABLE-US-00005 TABLE 5 Biomarkers Identified in Smoker-NSCLC in
Aggregated Data SCF sR Renin Caspase-3 PTN CSK AMPM2 HSP90a
Contactin-5 RS7 Kallikrein 7 UBE2N OCAD1 LRIG3 MPIF-1 HSP70 IGFBP-2
PRKACA GSK-3alpha PARC granzymeA FSTL3 CD30 Ligand Ubiquitin + 1
PAFAH beta subunit Prothrombin NAGK Integrin a1b1 ERBB1 Cathepsin S
ERK-1 KPCI TCTP CSK21 BTK UBC9 CATC GAPDH, liver MK13 MK01 CK-MB
Cystatin C pTEN LDH-H1 RPS6KA3 b2-Microglobulin CNDP1 IL-15Ra UFM1
RAC1 Calpain I UFC1 C9 MAPKAPK3 Peroxiredoxin-1 FGF-17 IMB1 PKB
Endostatin BARK1 IDE Cyclophilin A Cathepsin H HSP90b C1s
Macrophage mannose BGH3 receptor CD30 Dtk BLC BMP-1 NACA XPNPEP1
SBDS RabGDPdissociation TNFsR-I inhibitor beta MIP-5 LYN DUS3 CCL28
METAP1 MMP-7 MK12
TABLE-US-00006 TABLE 6 Biomarkers Identified in Benign Nodule-NSCLC
by Site ERBB1 FGF-17 LRIG3 CD30Ligand HMG-1 LGMN YES Proteinase-3
C9 MEK1 MK13 BLC Macrophage IL-17B mannose receptor ApoA-I CATC
CNDP1 Cadherin-6 BMP-1
TABLE-US-00007 TABLE 7 Biomarkers Identified in Smoker-NSCLC by
Site Kallikrein 7 CSK Azurocidin SCF sR FYN b2-Microglobulin ERBB1
BLC OCAD1 C9 TCTP LGMN LRIG3 Midkine PKB AMPM2 FGF-17 XPNPEP1
HSP90a MEK1 Cadherin-6 sL-Selectin BMP-1 pTEN BTK LYN LYNB CNDP1
Integrin a1b1 DUS3 CDK5-p35 PKB gamma Carbonic anhydrase XIII
TABLE-US-00008 TABLE 8 Biomarkers Identified in Benign Nodule-NSCLC
in Blended Data Set YES Catalase PAFAH beta eIF-5 subunit MK13
Prothrombin AMPM2 TNFsR-I LRIG3 BTK TCPTP BLC HMG-1 DRG-1 BGH3
MAPKAPK3 ERBB1 UBE2N Ubiquitin + 1 b2-Microglobulin Cadherin E
Activin A BARK1 SOD CK-MB TCTP LYN GSK-3 alpha C9 UBC9 PRKACA
Fibrinogen SCFsR NAGK LGMN ERK-1 CNDP1 Calpain I Integrin a1b1
Cadherin-6 RGM-C GAPDH HSP70 IDE METAP1 UFM1 XPNPEP1 UFC1
Macrophage Caspase-3 Stress-induced- PSA-ACT mannose receptor
phosphoprotein1 BMP-1 b-ECGF RPS6KA3 CATC KPCI RAC1 SHP-2 pTEN
IGFBP-2 MDHC CEA PSA CSK Proteinase-3 OCAD1 CATE NACA MK01
Cyclophilin A Peroxiredoxin-1 IMB1 MEK1 RabGDP SBDS dissociation
inhibitor beta Cathepsin H HSP90a DUS3 RS7 MMP-7 Thrombin CAMK2A
Carbonic anhydrase XIII VEGF FGF-17 CaMKKalpha HSP90b ART CSK21
TABLE-US-00009 TABLE 9 Biomarkers Identified in Smoker-NSCLC in
Blended Data Set SCFsR UBE2N CystatinC GSK-3alpha LRIG3 MIP-5 LYN
CATC HSP90a Contactin-5 MPIF-1 SBDS ERBB1 Ubiquitin + 1 GCP-2 PAFAH
beta subunit C9 Macrophage mannose KPCI IMB1 receptor AMPM2 PRKACA
MK12 CSK21 Kallikrein 7 Cathepsin S MAPKAPK3 PKB PTN BMP-1 Integrin
a1b1 Dtk PARC Cyclophilin A HSP70 DUS3 CD30 Ligand CCL28 RPS6KA3
Calpain I Prothrombin Endostatin NACA TNFsR-I CSK Cathepsin H RS7
PTP-1B CK-MB Granzyme A Peroxiredoxin-1 IDE BTK GAPDH, liver MMP-7
HSP90b C1s FGF-17 pTEN Fibrinogen IGFBP-2 BARK1 UFM1 Caspase-3
LDH-H1 BLC UBC9 PSA-ACT RAC1 RabGDP dissociation FSTL3 OCAD1
inhibitor beta Renin CD30 BGH3 SOD CNDP1 MK13 UFC1 METAP1 TCTP NAGK
MK01 PSA IL-15Ra b2-Microglobulin ERK-1
TABLE-US-00010 TABLE 10 Biomarkers for Lung Cancer Benign Nodule
Smokers AMPM2 YES SCFsR BMP-1 MK13 LRIG3 BTK LRIG3 HSP90a C1s HMG-1
ERBB1 C9 ERBB1 C9 Cadherin E CadherinE AMPM2 Catalase CK-MB
Kallikrein7 Cathepsin H C9 PTN CD30Ligand SCFsR PARC CK-MB CNDP1
CD30Ligand CNDP1 RGM-C Prothrombin Contactin-5 METAP1 CSK CSK
Macrophage CK-MB mannose receptor ERBB1 BMP-1 BTK HMG-1 KPCI C1s
HSP90a IGFBP-2 IGFBP-2 HSP90b CSK LDH-H1 IGFBP-2 NACA RAC1 IL-15Ra
IMB1 Renin IMB1 CathepsinH CNDP1 Kallikrein7 MMP-7 TCTP KPCI VEGF
IL-15Ra LDH-H1 HSP90b UBE2N LRIG3 Catalase MIP-5 Macrophage mannose
receptor Prothrombin Contactin-5 METAP1 ApoA-I Ubiquitin + 1 MIP-5
b-ECGF BLC MK13 BLC BMP-1 MMP-7 Cadherin-6 CDK5-p35 NACA Calpain I
Cyclophilin A PARC CATC Endostatin Prothrombin CD30Ligand FGF-17
PTN FGF-17 FYN RAC1 GAPDH GAPDH Renin HSP90a KPCI RGM-C IL-17B MEK1
SCF sR LGMN Midkine TCTP MEK1 sL-Selectin UBE2N NAGK Ubiquitin + 1
Proteinase-3 VEGF YES ApoA-I b-ECGF BLC Cadherin-6 Calpain I CATC
CDK5-p35 CyclophilinA Endostatin FYN FGF-17 GAPDH IL-17B LGMN MEK1
Midkine NAGK Proteinase-3 sL-Selectin
TABLE-US-00011 TABLE 11 Aptamer To Assay Up or Designated Solution
K.sub.d LLOQ Down Biomarker (M) (M) Regulated AMPM2 3 .times.
10.sup.-10 NM Up Apo A-I 9 .times. 10.sup.-09 2 .times. 10.sup.-11
Down .beta.-ECGF 1 .times. 10.sup.-10 NM Up (pool) BLC 5 .times.
10.sup.-10 7 .times. 10.sup.-14 Up (pool) BMP-1 2 .times.
10.sup.-10 9 .times. 10.sup.-13 Down BTK 8 .times. 10.sup.-10 2
.times. 10.sup.-13 Up (pool) C1s 8 .times. 10.sup.-09 7 .times.
10.sup.-12 Up C9 1 .times. 10.sup.-11 1 .times. 10.sup.-14 Down
Cadherin E 3 .times. 10.sup.-10 2 .times. 10.sup.-12 Down
Cadherin-6 2 .times. 10.sup.-09 2 .times. 10.sup.-12 Up Calpain I 2
.times. 10.sup.-11 7 .times. 10.sup.-14 Up Catalase 7 .times.
10.sup.-10 8 .times. 10.sup.-14 Up (pool) CATC 8 .times. 10.sup.-08
NM Up Cathepsin H 1 .times. 10.sup.-09 8 .times. 10.sup.-13 Up
(pool) CD30 Ligand 2 .times. 10.sup.-09 7 .times. 10.sup.-13 Up
(pool) CDK5/p35 2 .times. 10.sup.-10 NM Up CK-MB 1 .times.
10.sup.-08 NM Down (pool) CNDP1 3 .times. 10.sup.-08 NM Down
Contactin-5 3 .times. 10.sup.-11 NM Down CSK 3 .times. 10.sup.-10 5
.times. 10.sup.-13 Up Cyclophilin A 1 .times. 10.sup.-09 2 .times.
10.sup.-13 Up (pool) Endostatin 5 .times. 10.sup.-10 1 .times.
10.sup.-13 Up ERBB1 1 .times. 10.sup.-10 4 .times. 10.sup.-14 Down
FGF-17 5 .times. 10.sup.-10 NM Up (pool) FYN 3 .times. 10.sup.-09
NM Up (pool) GAPDH 8 .times. 10.sup.-12 4 .times. 10.sup.-13 Up
HMG-1 2 .times. 10.sup.-10 1 .times. 10.sup.-12 Up HSP 90.alpha. 1
.times. 10.sup.-10 1 .times. 10.sup.-12 Up HSP90.beta. 2 .times.
10.sup.-10 4 .times. 10.sup.-12 Up IGFBP-2 6 .times. 10.sup.-10 9
.times. 10.sup.-13 Up IL-15 R.alpha. 4 .times. 10.sup.-11 1 .times.
10.sup.-13 Up (pool) IL-17B 3 .times. 10.sup.-11 4 .times.
10.sup.-13 Up (pool) IMB1 8 .times. 10.sup.-08 NM Up (pool)
Kallikrein 7 6 .times. 10.sup.-11 2 .times. 10.sup.-12 Down KPCI 9
.times. 10.sup.-09 NM Up LDH-H1 1 .times. 10.sup.-09 8 .times.
10.sup.-13 Up LGMN 7 .times. 10.sup.-09 NM Up LRIG3 3 .times.
10.sup.-11 8 .times. 10.sup.-14 Down Macrophage 1 .times.
10.sup.-09 1 .times. 10.sup.-11 Up mannose receptor MEK1 6 .times.
10.sup.-10 NM Up METAP1 7 .times. 10.sup.-11 9 .times. 10.sup.-13
Up Midkine 2 .times. 10.sup.-10 4 .times. 10.sup.-11 Up MIP-5 9
.times. 10.sup.-09 2 .times. 10.sup.-13 Up (pool) MK13 2 .times.
10.sup.-09 NM Up MMP-7 7 .times. 10.sup.-11 3 .times. 10.sup.-13 Up
NACA 2 .times. 10.sup.-11 NM Up NAGK 2 .times. 10.sup.-09 NM Up
(pool) PARC 9 .times. 10.sup.-11 1 .times. 10.sup.-13 Up
Proteinase-3 5 .times. 10.sup.-09 4 .times. 10.sup.-12 Up (pool)
Prothrombin 5 .times. 10.sup.-09 1 .times. 10.sup.-12 Down PTN 4
.times. 10.sup.-11 5 .times. 10.sup.-12 Up RAC1 7 .times.
10.sup.-11 NM Up Renin 3 .times. 10.sup.-11 3 .times. 10.sup.-13 Up
RGM-C 3 .times. 10.sup.-11 NM Down SCF sR 5 .times. 10.sup.-11 3
.times. 10.sup.-12 Down sL-Selectin 2 .times. 10.sup.-10 2 .times.
10.sup.-13 Down (pool) TCTP 2 .times. 10.sup.-11 NM Up (pool) UBE2N
6 .times. 10-11 NM Up (pool) Ubiquitin + 1 2 .times. 10.sup.-10 1
.times. 10.sup.-12 Up VEGF 4 .times. 10.sup.-10 9 .times.
10.sup.-14 Up YES 2 .times. 10.sup.-09 NM Up
TABLE-US-00012 TABLE 12 Parameters for Smoker Control Group
Biomarker # from Table 1 Biomarker .mu..sub.c .sigma..sub.c.sup.2
.mu..sub.d .sigma..sub.d.- sup.2 KS p-value AUC 1 AMPM2 3.05
1.07E-02 3.20 3.62E-02 0.45 5.55E-24 0.75 4 BLC 2.58 1.23E-02 2.72
3.97E-02 0.37 8.72E-17 0.74 5 BMP-1 4.13 1.32E-02 4.00 2.01E-02
0.38 1.21E-17 0.75 6 BTK 3.12 2.44E-01 3.51 2.45E-01 0.35 3.25E-15
0.72 7 C1s 4.01 3.47E-03 4.06 4.23E-03 0.31 4.68E-12 0.69 8 C9 5.31
3.54E-03 5.38 5.37E-03 0.43 3.49E-22 0.75 15 CD30 3.21 2.86E-03
3.26 4.42E-03 0.31 1.08E-11 0.70 Ligand 16 CDK5-p35 2.98 3.48E-03
3.02 4.75E-03 0.25 1.63E-07 0.67 17 CK-MB 3.25 5.18E-02 3.07
4.89E-02 0.33 1.42E-13 0.71 18 CNDP1 3.65 1.97E-02 3.52 3.07E-02
0.36 4.14E-16 0.73 19 Contactin-5 3.66 9.35E-03 3.59 1.33E-02 0.31
1.67E-11 0.68 20 CSK 3.25 6.59E-02 3.54 1.10E-01 0.41 1.33E-20 0.76
21 CyclophilinA 4.42 6.04E-02 4.65 6.80E-02 0.38 2.17E-17 0.73 22
Endostatin 4.61 4.29E-03 4.67 1.07E-02 0.32 1.42E-12 0.69 23 ERBB1
4.17 2.25E-03 4.10 5.18E-03 0.47 9.39E-27 0.78 24 FGF-17 3.08
1.12E-03 3.11 1.31E-03 0.32 1.07E-12 0.71 25 FYN 3.18 6.88E-02 3.24
7.99E-02 0.13 1.53E-02 0.58 26 GAPDH 3.26 7.32E-02 3.51 1.62E-01
0.40 2.02E-19 0.68 28 HSP90a 4.45 1.86E-02 4.61 1.86E-02 0.50
3.09E-30 0.80 30 IGFBP-2 4.30 3.42E-02 4.48 4.17E-02 0.37 5.40E-17
0.74 31 IL-15 Ra 3.03 9.74E-03 3.12 2.10E-02 0.31 7.31E-12 0.69 34
Kallikrein 7 3.52 8.67E-03 3.44 1.21E-02 0.36 2.47E-15 0.70 35 KPCI
2.58 2.92E-03 2.66 1.01E-02 0.40 2.30E-19 0.74 36 LDH-H1 3.60
8.03E-03 3.67 1.45E-02 0.32 3.70E-12 0.68 38 LRIG3 3.55 3.10E-03
3.50 3.60E-03 0.36 1.39E-15 0.72 40 MEK1 2.81 1.54E-03 2.84
2.75E-03 0.28 1.96E-09 0.67 42 Midkine 3.21 3.13E-02 3.24 5.58E-02
0.13 1.90E-02 0.56 43 MIP-5 3.60 3.65E-02 3.77 5.88E-02 0.34
8.40E-14 0.70 48 PARC 4.90 1.94E-02 5.01 2.13E-02 0.34 7.01E-14
0.71 50 Prothrombin 4.68 5.37E-02 4.53 4.31E-02 0.32 1.09E-12 0.68
51 PTN 3.73 7.08E-03 3.80 7.36E-03 0.34 3.97E-14 0.72 52 RAC1 3.85
6.13E-02 4.09 7.31E-02 0.40 4.60E-19 0.72 53 Renin 3.25 2.52E-02
3.39 6.36E-02 0.30 4.23E-11 0.68 55 SCF sR 3.79 1.11E-02 3.68
1.48E-02 0.37 9.90E-17 0.75 56 sL-Selectin 4.46 5.63E-03 4.40
9.30E-03 0.30 6.24E-11 0.69 57 TCTP 4.19 4.69E-02 4.44 7.43E-02
0.43 9.69E-22 0.76 58 UBE2N 4.42 9.30E-02 4.67 9.53E-02 0.34
6.56E-14 0.72 59 Ubiquitin + 1 4.25 1.75E-02 4.34 1.43E-02 0.31
1.55E-11 0.68
TABLE-US-00013 TABLE 13 Parameters for benign nodules control group
Biomarker # from Table 1 Biomarker .mu..sub.c .sigma..sub.c.sup.2
.mu..sub.d .sigma..sub.d.- sup.2 KS p-value AUC 2 ApoA-I 3.83
1.04E-02 3.77 1.56E-02 0.24 1.67E-07 0.65 3 b-ECGF 3.03 1.27E-03
3.06 1.53E-03 0.30 7.50E-12 0.68 4 BLC 2.60 1.50E-02 2.72 3.97E-02
0.31 1.77E-12 0.70 5 BMP-1 4.11 1.39E-02 4.00 2.01E-02 0.32
2.00E-13 0.72 8 C9 5.31 4.84E-03 5.38 5.37E-03 0.39 9.42E-20 0.75 9
Cadherin E 4.51 5.91E-03 4.43 9.86E-03 0.37 1.93E-17 0.74 10
Cadherin-6 2.91 3.79E-03 2.98 1.12E-02 0.36 1.42E-16 0.72 11
Calpain I 4.37 1.33E-02 4.50 2.32E-02 0.40 7.63E-21 0.75 12
Catalase 4.27 2.09E-02 4.37 1.30E-02 0.34 4.30E-15 0.72 13 CATC
2.80 5.83E-03 2.86 7.63E-03 0.31 8.55E-13 0.69 14 Cathepsin H 4.59
3.24E-03 4.63 7.54E-03 0.30 4.29E-12 0.66 15 CD30 Ligand 3.21
4.19E-03 3.26 4.42E-03 0.26 4.70E-09 0.68 17 CK-MB 3.23 4.47E-02
3.07 4.89E-02 0.32 2.76E-13 0.70 18 CNDP1 3.65 2.03E-02 3.52
3.07E-02 0.35 2.04E-15 0.72 20 CSK 3.25 7.98E-02 3.54 1.10E-01 0.41
2.35E-21 0.76 23 ERBB1 4.17 2.76E-03 4.10 5.18E-03 0.46 1.22E-26
0.77 24 FGF-17 3.08 1.26E-03 3.11 1.31E-03 0.31 9.59E-13 0.71 26
GAPDH 3.22 7.96E-02 3.51 1.62E-01 0.40 7.88E-21 0.69 27 HMG-1 4.01
4.57E-02 4.19 7.55E-02 0.30 1.99E-11 0.70 28 HSP90a 4.43 2.23E-02
4.61 1.86E-02 0.51 1.26E-33 0.81 29 HSP90b 3.06 3.70E-03 3.14
9.67E-03 0.42 2.73E-22 0.75 30 IGFBP-2 4.32 3.57E-02 4.48 4.17E-02
0.35 2.30E-15 0.73 32 IL-17B 2.19 3.73E-03 2.23 4.16E-03 0.28
3.65E-10 0.68 33 IMB1 3.47 2.21E-02 3.67 5.45E-02 0.42 2.04E-22
0.75 35 KPCI 2.57 3.26E-03 2.66 1.01E-02 0.43 3.57E-23 0.75 37 LGMN
3.13 2.03E-03 3.17 4.15E-03 0.30 1.15E-11 0.69 38 LRIG3 3.55
3.59E-03 3.50 3.60E-03 0.33 9.00E-14 0.71 39 Macrophage 4.10
1.51E-02 4.22 2.48E-02 0.36 7.24E-17 0.72 mannose receptor 40 MEK1
2.81 1.77E-03 2.84 2.75E-03 0.31 3.79E-12 0.69 41 METAP1 2.67
2.45E-02 2.89 5.83E-02 0.44 2.99E-24 0.75 44 MK13 2.79 3.38E-03
2.85 4.88E-03 0.36 6.16E-17 0.74 45 MMP-7 3.64 3.24E-02 3.82
4.85E-02 0.37 1.89E-17 0.73 46 NACA 3.11 8.28E-03 3.21 2.63E-02
0.34 4.91E-15 0.70 47 NAGK 3.71 2.04E-02 3.84 2.63E-02 0.38
7.50E-19 0.73 49 Proteinase-3 3.95 9.09E-02 4.18 1.23E-01 0.30
2.22E-11 0.69 50 Prothrombin 4.67 4.19E-02 4.53 4.31E-02 0.32
2.17E-13 0.68 54 RGM-C 4.44 4.85E-03 4.38 6.13E-03 0.30 1.00E-11
0.69 55 SCF sR 3.77 9.71E-03 3.68 1.48E-02 0.35 1.96E-15 0.72 60
VEGF 3.55 8.80E-03 3.62 1.14E-02 0.30 1.27E-11 0.69 61 YES 2.97
9.54E-04 3.00 1.73E-03 0.29 7.59E-11 0.67
TABLE-US-00014 TABLE 14 Sensitivity + Specificity for Exemplary
Combinations of Biomarkers Sen- Spe- Sensitivity + # sitivity
cificity Specificity AUC 1 SCFsR 0.629 0.727 1.356 0.75 2 SCFsR
HSP90a 0.761 0.753 1.514 0.84 3 SCFsR HSP90a ERBB1 0.775 0.827
1.602 0.87 4 SCFsR HSP90a ERBB1 PTN 0.784 0.861 1.645 0.89 5 SCFsR
HSP90a ERBB1 PTN BTK 0.84 0.844 1.684 0.9 6 SCFsR HSP90a ERBB1 PTN
BTK CD30 0.822 0.869 1.691 0.9 Ligand 7 SCFsR HSP90a ERBB1 PTN BTK
CD30 Kallikrein7 0.845 0.875 1.72 0.91 Ligand 8 SCFsR HSP90a ERBB1
PTN BTK CD30 Kallikrein7 LRIG3 0.859 0.864 1.723 0.- 91 Ligand 9
SCFsR HSP90a ERBB1 PTN BTK CD30 Kallikrein7 LRIG3 LDH- 0.869 0.872
1.74- 1 0.91 Ligand H1 10 SCFsR HSP90a ERBB1 PTN BTK CD30
Kallikrein7 LRIG3 LDH- PARC 0.873 0.878- 1.751 0.91 Ligand H1
TABLE-US-00015 TABLE 15 Parameters derived from training set for
naive Bayes classifier. Biomarker .mu..sub.c .mu..sub.d
.sigma..sub.c .sigma..sub.d {tilde over (x)} p (c|{tilde over (x)})
p (d|{tilde over (x)}) ln (p(c|{tilde over (x)})/p(d|{tilde over
(x)})) C9 11.713 11.934 0.199 0.210 11.667 1.946 0.843 0.836 LRIG3
7.409 7.307 0.090 0.084 7.372 4.058 3.511 0.145 GAPDH 9.027 9.385
0.511 0.230 9.000 0.780 0.428 0.599 MMP12 6.139 6.346 0.096 0.255
6.129 4.115 1.087 1.332 KLK7 8.130 7.979 0.230 0.298 8.419 0.789
0.450 0.562
TABLE-US-00016 TABLE 16 Naive Bayes parameters for all markers in
Table 21 for both tissue and serum Tissue Tissue Tissue Tissue
Serum Serum Serum Serum Biomarker .mu..sub.c .mu..sub.d
.sigma..sub.c .sigma..sub.d u.sub.c .mu..s- ub.d .sigma..sub.c
.sigma..sub.d Activin A 5.927 6.713 0.124 0.816 6.990 7.060 0.089
0.120 Adiponectin 9.357 8.560 0.456 0.154 8.986 9.141 0.406 0.391
AMPM2 9.352 9.916 0.393 0.313 7.067 7.079 0.091 0.107 Apo A-I 6.554
6.573 0.312 0.271 8.699 8.593 0.139 0.130 b-ECGF 6.731 7.256 0.606
0.739 6.205 6.160 0.056 0.072 BGN 7.989 6.821 0.653 0.168 7.140
7.067 0.125 0.077 BLC 6.283 7.776 0.253 0.971 7.065 7.058 0.066
0.072 BMP-1 5.149 5.377 0.071 0.239 8.766 8.548 0.213 0.234 BTK
8.782 7.757 0.547 1.303 7.567 7.856 0.464 0.304 C1s 7.989 7.973
0.206 0.298 8.532 8.540 0.106 0.121 C9 11.488 11.417 0.325 0.380
11.715 11.936 0.189 0.223 Cadherin-6 7.129 7.502 0.034 0.265 7.971
7.959 0.087 0.067 Cadherin E 7.370 7.916 0.458 0.349 9.252 9.050
0.200 0.181 Calpain I 9.641 9.962 0.503 0.553 10.358 10.466 0.132
0.143 Carbonic anhydrase III 9.096 7.504 0.288 0.890 8.552 8.687
0.474 0.351 Caspase-3 6.758 7.426 0.248 0.110 7.097 7.136 0.338
0.367 Catalase 11.253 10.581 0.127 0.633 10.051 10.243 0.392 0.276
CATC 7.839 7.783 0.457 0.385 7.248 7.229 0.088 0.088 Cathepsin H
12.452 11.758 0.158 0.410 9.485 9.585 0.124 0.210 CD30 Ligand 6.655
6.911 0.069 0.168 7.622 7.605 0.038 0.035 CD36 ANTIGEN 7.026 6.262
0.478 0.175 8.252 8.224 0.114 0.141 CDK5/p35 6.581 6.741 0.210
0.215 6.986 7.044 0.083 0.075 CK-MB 8.912 8.564 0.703 0.611 7.515
7.230 0.317 0.307 CNDP1 7.293 7.292 0.062 0.189 9.995 9.754 0.295
0.375 Contactin-5 5.793 5.777 0.063 0.146 6.749 6.689 0.109 0.141
CSK 8.526 8.181 0.370 0.736 6.809 7.186 0.388 0.245 CXCL16, soluble
8.216 7.559 0.517 0.449 9.660 9.744 0.185 0.230 Cyclophilin A
11.751 11.668 0.159 0.123 8.586 8.784 0.323 0.233 Endostatin 8.669
8.096 0.233 0.374 8.763 8.876 0.125 0.162 ERBB1 7.041 7.263 0.134
0.336 10.578 10.428 0.119 0.135 ESAM 8.659 7.451 0.376 0.473 9.022
9.033 0.151 0.142 FGF-17 6.111 5.998 0.036 0.066 6.897 6.902 0.062
0.069 Fibronectin 9.681 10.795 0.452 1.097 11.288 11.105 0.253
0.269 FYN 8.003 7.834 0.149 0.262 8.002 8.033 0.123 0.086 GAPDH,
liver 12.703 12.713 0.123 0.152 9.033 9.410 0.536 0.194 HMG-1
11.639 11.541 0.545 0.615 8.430 8.546 0.133 0.096 HSP 90a 11.569
11.820 0.479 0.279 9.165 9.343 0.226 0.182 HSP 90b 8.509 9.422
0.974 0.960 7.635 7.653 0.053 0.059 IDE 8.426 9.023 0.362 0.302
7.670 7.728 0.096 0.106 IGFBP-2 7.715 9.591 0.416 1.413 8.514 9.006
0.417 0.448 IGFBP-5 7.619 9.347 0.282 1.263 9.705 9.675 0.126 0.138
IGFBP-7 8.999 9.843 0.717 0.307 9.251 9.156 0.148 0.172 IL-15 Ra
6.088 6.577 0.123 0.318 7.068 7.066 0.056 0.071 IL-17B 5.441 5.531
0.051 0.139 6.267 6.257 0.052 0.066 IL-8 7.037 8.206 0.145 0.631
7.114 7.109 0.052 0.066 IMB1 5.867 6.218 1.300 1.010 7.326 7.390
0.150 0.152 Kallikrein 7 5.990 6.152 0.146 0.447 8.132 7.964 0.221
0.295 KPCI 6.589 6.821 0.244 0.420 6.195 6.194 0.053 0.046 LDH-H 1
12.527 12.640 0.135 0.169 7.221 7.261 0.140 0.198 LGMN 7.964 8.124
0.084 0.101 8.404 8.377 0.074 0.070 LRIG3 6.198 6.213 0.383 0.336
7.411 7.301 0.090 0.092 Macrophage 6.738 5.654 0.394 0.440 8.132
8.233 0.203 0.253 mannose receptor MEK1 6.543 6.657 0.305 0.505
5.979 5.966 0.039 0.048 METAP1 9.004 9.807 0.540 0.412 7.955 7.982
0.095 0.081 Midkine 6.619 7.223 0.770 1.112 7.714 7.714 0.298 0.193
MIP-5 5.582 5.657 0.041 0.090 8.560 8.659 0.262 0.233 MK13 7.195
7.793 0.260 0.491 NA NA NA NA MMP-12 5.822 8.677 0.182 1.045 6.129
6.323 0.100 0.260 MMP-7 6.800 8.224 0.440 0.215 8.881 9.232 0.235
0.182 NACA 6.480 6.738 0.207 0.183 7.774 7.791 0.111 0.108 NAGK
9.469 9.986 0.328 0.457 7.385 7.476 0.203 0.216 NAP-2 10.672 9.447
0.357 0.842 7.765 7.775 0.286 0.342 PARC 9.519 9.315 0.537 0.169
10.087 10.291 0.424 0.369 Proteinase-3 7.667 6.963 0.789 0.850
8.340 8.394 0.461 0.504 Prothrombin 7.245 7.400 0.443 0.390 NA NA
NA NA P-Selectin 7.947 6.593 0.263 0.508 9.937 9.944 0.278 0.199
PTN 7.363 7.301 0.492 0.531 8.149 8.250 0.116 0.152 RAC1 11.522
11.299 0.109 0.220 8.408 8.697 0.378 0.323 Renin 5.964 5.894 0.039
0.080 7.675 7.797 0.338 0.506 RGM-C 6.677 6.646 0.049 0.084 9.765
9.700 0.164 0.180 SCF sR 6.607 6.639 0.163 0.175 9.603 9.503 0.139
0.141 SLPI 10.635 9.435 0.676 0.476 NA NA NA NA sL-Selectin 6.524
6.827 0.149 0.166 NA NA NA NA sRAGE 11.154 7.304 0.619 0.912 7.001
6.845 0.333 0.297 TCTP 10.524 10.395 0.087 0.127 8.847 9.137 0.290
0.224 Thrombospondin-1 9.012 10.305 0.520 1.093 9.187 8.950 0.558
0.349 TPSB2 10.798 9.138 0.668 1.055 7.714 7.435 0.346 0.441
TrATPase 11.031 8.887 0.993 0.703 9.099 9.168 0.204 0.148 TSP2
6.569 7.837 0.085 0.627 7.468 7.562 0.162 0.218 UBE2N 10.654 10.725
0.166 0.140 9.234 9.487 0.521 0.288 Ubiquitin + 1 10.948 10.860
0.249 0.275 9.218 9.284 0.249 0.171 uPA 5.747 6.564 0.119 0.445
6.868 6.874 0.104 0.126 URB 7.180 8.539 0.283 0.699 8.689 8.756
0.173 0.202 VEGF 6.313 7.593 0.088 1.074 7.699 7.769 0.096 0.145
vWF 7.927 7.193 0.263 0.139 10.531 10.684 0.236 0.200 YES 7.086
7.723 0.386 0.314 6.593 6.605 0.065 0.067
TABLE-US-00017 TABLE 17 Patient demographics, resection location
and tumor types for the eight NSCLC samples Smoking Age Sex History
Location Stage Tissue Dx 47 F Smoker Left Upper Lobe pT3pN1pMx
stage IIIA Poorly differentiated non-small cell CA with focal
Squamous differentiation 73 F Smoker Left Lower Lobe pT2pN0pMx
stage IB Poorly differentiated Squamous cell carcinoma 48 M Smoker
Right Upper Lobe pT2pN1pMx stage IIIA Poorly differentiated
Squamous cell carcinoma 60 F Smoker Left Upper Lobe T4 N1 M0 stage
IIIB-- Poorly differentiated Squamous cell note T4 distinction
based carcinoma on clinical lung collapse; tumor was pT2 by size
criteria 51 F Smoker Right Upper Lobe pT2pN0pMx stage IB Moderately
differentiated Adenocarcinoma 71 F Smoker Right Upper Lobe
pT2pN0pMx stage IB Well differentiated Adenocarcinoma 75 F Smoker
Right Lower Lobe pT1N0Mx Stage IA Well differentiated
Adenocarcinoma 73 M Smoker Left Upper Lobe pT1bN0Mx Stage IA
Atypical Carcinoid Tumor (i.e. neuroendocrine, IHC positive for
chromogranin)
TABLE-US-00018 TABLE 18 Differentially Expressed Biomarkers Between
Tumor and Normal Tissue Up/Down Biomarker # Biomarker Designation
Alternate Protein Names Gene Regulated 1 Activin A Inhibin beta A
chain INHBA up Activin beta-A chain Erythroid differentiation
protein EDF 2 Adiponectin 30 kDa adipocyte complement-related
protein ADIPOQ down Adipocyte complement-related 30 kDa protein
ACRP30 Adipocyte, C1q and collagen domain-containing protein
Adipose most abundant gene transcript 1 protein apM-1
Gelatin-binding protein Adipolean 3 BCA-1* C--X--C motif chemokine
13 CXCL13 up Angie B cell-attracting chemokine 1 B lymphocyte
chemoattractant CXC chemokine BLC Small-inducible cytokine B13 BLC
4 Biglycan Bone/cartilage proteoglycan I BGN down PG-S1 5
Cadherin-1* CAM 120/80 CDH1 up Epithelial cadherin E-cadherin
Uvomorulin CD324 6 Carbonic anhydrase III Carbonic anhydrase 3 CA3
down Carbonate dehydratase III CA-III 7 Caspase-3 CASP-3 CASP3 up
Apopain Cysteine protease CPP32 CPP-32 Protein Yama SREBP cleavage
activity 1 SCA-1 8 Catalase* CAT down 9 CD36 Antigen Platelet
glycoprotein 4 CD36 down Fatty acid translocase FAT Glycoprotein
IIIb GPIIIB Leukocyte differentiation antigen CD36 PAS IV PAS-4
Platelet collagen receptor Platelet glycoprotein IV GPIV
Thrombospondin receptor 10 CXCL16, soluble C--X--C motif chemokine
16 CXCL16 down Scavenger receptor for phosphatidylserine and
oxidized low density lipoprotein SR-PSOX Small-inducible cytokine
B16 Transmembrane chemokine CSCL16 11 Endostatin* COL18A1 down 12
ESAM Endothelial cell-selective adhesion molecule ESAM down 13
Fibronectin FN FN1 up Cold-insoluble globulin CIG FNT 14 Insulysin
Insulin-degrading enzyme IDE up Abeta-degrading protease Insulin
protease Insulinase 15 IGFBP-2* Insulin-like growth factor-binding
protein 2 IGFBP2 up IBP-2 IGF-binding protein 2 16 IGFBP-5
Insulin-like growth factor-binding protein 5 IGFBP5 up IBP-5
IGF-binding protein 5 17 IGFBP-7 Insulin-like growth factor-binding
protein 7 IGFBP7 up IBP-7 IGF-binding protein 7 IGFBP-rP1 MAC25
protein PGI2-stimulating factor Prostacyclin-stimulating factor
Tumor-derived adhesion factor TAF 18 IL-8 Interleukin-8 IL8 up
C--X--C motif chemokine 8 Emoctakin Granulocyte chemotactic protein
1 GCP-1 Monocyte-derived neutrophil chemotactic factor MDNCF
Monocyte-derived neutrophil-activating peptide MONAP
Neutrophil-activating protein NAP-1 Protein 3-10C T-cell
chemotactic factor 19 MRC1* Macrophage mannose receptor 1 MRC1 down
MMR C-type lectin domain family 13 member D C-type lectin domain
family 13 member D-like Macrophage mannose receptor 1-like protein
1 CD206 20 MAPK13* Mitogen-activated protein kinase 13 MAPK13 up
MAP kinase 13 Mitogen-activated protein kinase p38 delta MAP kinase
p38 delta Stress-activated protein kinase 4 21 MMP-7* Matrilysin
MMP7 up Matrin Matrix metalloproteinase-7 Pump-1 protease Uterine
metalloproteinase 22 MMP-12* Macrophage metalloelastase MMP12 up
MME Macrophage elastase ME hME Matrix metalloproteinase-12 23 NAGK*
N-acetyl-D-glucosamine kinase NAGK up N-acetylglucosamine kinase
GlcNAc kinase 24 NAP-2 Neutrophil-activating peptide 2 PPBP down 25
P-Selectin CD62 antigen-like family member P SELP down Granule
membrane protein 140 GMP-140 Leukocyte-endothelial cell adhesion
molecule 3 LECAM-3 Platelet activation dependent granule-external
membrane protein PADGEM CD62P 26 SLPI Antileukoproteinase SLPI down
ALP BLPI HUSI-1 Mucus proteinase inhibitor MPI Protease inhibitor
WAP4 Secretory leukocyte protease inhibitor Seminal proteinase
inhibitor WAP four-disulfide core domain protein 4 27 sRAGE
Advanced glycosylation end product-specific AGER down receptor
Receptor for advanced glycosylation end products 28
Thrombospondin-1 TSP-1 THBS1 up 29 Thrombospondin-2 TSP-2 THBS1 up
30 TrATPase Tartrate-resistant acid phosphatase type 5 ACP5 down
TR-AP Tartrate-resistant acid ATPase Type 5 acid phosphatase 31
Tryptase .beta.-2 Tryptase beta-2 TPSB2 down Tryptase-2 Tryptase II
TRYB2 32 uPA Urokinase-type plasminogen activator PLAU up
U-plasminogen activator Urokinase 33 URB Coiled-coil
domain-containing protein 80 CCDC80 up Down-regulated by oncogenes
protein 1 Up-regulated in BRS-3 deficient mouse homolog 34 VEGF*
Vascular endothelial growth factor A VEGFA up VEGF-A Vascular
permeability factor VPF 35 vWF von Willebrand factor VWF down 36
YES* Tyrosine-protein kinase Yes YES1 up Proto-oncogene c-Yes
P61-Yes *Overlap of Biomarkers Expressed in both Serum and Tumor
Tissue
TABLE-US-00019 TABLE 19 Categorization of NSCLC tissue biomarkers
into biological processes Invasion, Growth and Inflammation &
Metastasis Angiogenesis Metabolism Apoptosis (ECM) VEGF
Adiponectin* Activin A Biglycan* Endostatin Carbonic BCA-1*
Cadherin-1 anhydrase III* Thrombospondin-1 IGFBP-2 Catalase CD36
Antigen Thrombospondin-2 IGFBP-5 CXCL16, ESAM soluble* IGFBP-7 IL-8
Fibronectin* Insulysin* MRC1* MMP-7 NAGK* NAP-2 MMP-12 TrATPase*
sRAGE P-Selectin* Tryptase b-2 SLPI URB* MAPK13* uPA vWF Caspase-3
Thrombos- pondin-1 Thrombos- pondin-2 YES *Novel NSCLC
Biomarker
TABLE-US-00020 TABLE 20 Biomarkers Identified in NSCLC Tissue*
Biomarker # Biomarker Designation 1 Activin A 2 Adiponectin 3
Biglycan 4 Carbonic anhydrase III 5 Caspase-3 6 CD36 Antigen 7
CXCL16, soluble 8 ESAM 9 Fibronectin 10 Insulysin 11 IGFBP-5 12
IGFBP-7 13 IL-8 14 MMP-12 15 NAP-2 16 P-Selectin 17 SLPI 18 sRAGE
19 Thrombospondin-1 20 Thrombospondin-2 21 TrATPase 22 Tryptase
.beta.-2 23 uPA 24 URB 25 vWF *This list excludes biomarkers which
were identified in both tissue and serum samples
TABLE-US-00021 TABLE 21 Biomarkers Identified in Serum and Tissue
Biomarker Biomarker Designation 1 Activin A 2 Adiponectin 3 AMPM2 4
Apo A-I 5 Biglycan 6 b-ECGF 7 BLC* 8 BMP-1 9 BTK 10 C1s 11 C9 12
Cadherin E* 13 Cadherin-6 14 Calpain I 15 Carbonic anhydrase III 16
Caspase-3 17 Catalase* 18 CATC 19 Cathepsin H 20 CD30 Ligand 21
CD36 Antigen 22 CDK5-p35 23 CK-MB 24 CNDP1 25 Contactin-5 26 CSK 27
CXCL16, soluble 28 Cyclophilin A 29 Endostatin* 30 ERBB1 31 ESAM 32
FGF-17 33 Fibronectin 34 FYN 35 GAPDH, liver 36 HMG-1 37 HSP 90a 38
HSP 90b 39 IGFBP-2* 40 IGFBP-5 41 IGFBP-7 42 IL-8 43 IL-15 Ra 44
IL-17B 45 IMB1 46 Insulysin 47 Kallikrein 7 48 KPCI 49 LDH-H 1 50
LGMN 51 LRIG3 52 Macrophage mannose receptor* 53 MEK1 54 METAP1 55
Midkine 56 MIP-5 57 MK13* 58 MMP-7* 59 MMP-12* 60 NACA 61 NAGK* 62
NAP-2 63 PARC 64 P-Selectin 65 Proteinase-3 66 Prothrombin 67 PTN
68 RAC1 69 Renin 70 RGM-C 71 SCF sR 72 SLPI 73 sL-Selectin 74 sRAGE
75 TCTP 76 Thrombospondin-1 77 Thrombospondin-2 78 TrATPase 79
Tryptase .beta.-2 80 UBE2N 81 Ubiquitin + 1 82 uPA 83 URB 84 VEGF*
85 vWF 86 YES* *Biomarkers identified in both serum in tissue
TABLE-US-00022 TABLE 22 81 Panels of two biomarkers including
MMP-12 Markers AUC 1 CSK MMP-12 0.848 2 GAPDH, liver MMP-12 0.842 3
Cyclophilin A MMP-12 0.832 4 TCTP MMP-12 0.831 5 C9 MMP-12 0.828 6
LRIG3 MMP-12 0.826 7 MMP-7 MMP-12 0.824 8 BMP-1 MMP-12 0.823 9 SCF
sR MMP-12 0.823 10 ERBB1 MMP-12 0.822 11 RAC1 MMP-12 0.822 12
Kallikrein 7 MMP-12 0.822 13 HSP 90a MMP-12 0.817 14 CDK5/p35
MMP-12 0.815 15 IGFBP-2 MMP-12 0.812 16 HMG-1 MMP-12 0.809 17
Cadherin E MMP-12 0.808 18 b-ECGF MMP-12 0.807 19 Calpain I MMP-12
0.805 20 RGM-C MMP-12 0.804 21 IMB1 MMP-12 0.802 22 UBE2N MMP-12
0.802 23 LGMN MMP-12 0.801 24 Catalase MMP-12 0.801 25 CK-MB MMP-12
0.800 26 BTK MMP-12 0.799 27 Endostatin MMP-12 0.791 28 BGN MMP-12
0.791 29 PTN MMP-12 0.790 30 CD30 Ligand MMP-12 0.789 31 Activin A
MMP-12 0.785 32 vWF MMP-12 0.784 33 TSP2 MMP-12 0.784 34 IL-8
MMP-12 0.782 35 Adiponectin MMP-12 0.781 36 Thrombospondin-1 MMP-12
0.779 37 NAGK MMP-12 0.777 38 MIP-5 MMP-12 0.776 39 VEGF MMP-12
0.776 40 NACA MMP-12 0.773 41 LDH-H 1 MMP-12 0.771 42 CNDP1 MMP-12
0.770 43 IGFBP-7 MMP-12 0.770 44 Proteinase-3 MMP-12 0.769 45 TPSB2
MMP-12 0.769 46 Apo A-I MMP-12 0.768 47 Macrophage mannose receptor
MMP-12 0.768 48 Ubiquitin + 1 MMP-12 0.767 49 IDE MMP-12 0.767 50
Cathepsin H MMP-12 0.766 51 CXCL16, soluble MMP-12 0.763 52
TrATPase MMP-12 0.762 53 Caspase-3 MMP-12 0.757 54 Cadherin-6
MMP-12 0.757 55 Contactin-5 MMP-12 0.756 56 BLC MMP-12 0.756 57
FGF-17 MMP-12 0.755 58 Fibronectin MMP-12 0.754 59 NAP-2 MMP-12
0.754 60 HSP 90b MMP-12 0.754 61 C1s MMP-12 0.753 62 AMPM2 MMP-12
0.752 63 IL-17B MMP-12 0.752 64 IL-15 Ra MMP-12 0.751 65 uPA MMP-12
0.750 66 PARC MMP-12 0.749 67 IGFBP-5 MMP-12 0.748 68 Renin MMP-12
0.745 69 KPCI MMP-12 0.742 70 METAP1 MMP-12 0.742 71 Carbonic
anhydrase III MMP-12 0.740 72 CATC MMP-12 0.740 73 MEK1 MMP-12
0.740 74 URB MMP-12 0.736 75 CD36 ANTIGEN MMP-12 0.735 76 Midkine
MMP-12 0.735 77 sRAGE MMP-12 0.731 78 ESAM MMP-12 0.729 79 YES
MMP-12 0.728 80 P-Selectin MMP-12 0.723 81 FYN MMP-12 0.707
TABLE-US-00023 TABLE 23 100 Panels of three biomarkers including
MMP-12 Markers AUC 1 C9 GAPDH, liver MMP-12 0.879 2 MMP-7 GAPDH,
liver MMP-12 0.876 3 C9 CSK MMP-12 0.875 4 BMP-1 CSK MMP-12 0.869 5
BMP-1 GAPDH, liver MMP-12 0.868 6 LRIG3 GAPDH, liver MMP-12 0.867 7
Kallikrein 7 GAPDH, liver MMP-12 0.867 8 MMP-7 CSK MMP-12 0.867 9
RAC1 C9 MMP-12 0.865 10 CSK Kallikrein 7 MMP-12 0.865 11 IGFBP-2
GAPDH, liver MMP-12 0.864 12 CDK5/p35 GAPDH, liver MMP-12 0.862 13
C9 TCTP MMP-12 0.862 14 C9 Cyclophilin A MMP-12 0.862 15 LRIG3 CSK
MMP-12 0.862 16 SCF sR CSK MMP-12 0.861 17 SCF sR GAPDH, liver
MMP-12 0.861 18 IGFBP-2 CSK MMP-12 0.860 19 MMP-7 TCTP MMP-12 0.860
20 b-ECGF GAPDH, liver MMP-12 0.860 21 ERBB1 CSK MMP-12 0.859 22
RAC1 BMP-1 MMP-12 0.858 23 LRIG3 TCTP MMP-12 0.858 24 ERBB1 GAPDH,
liver MMP-12 0.857 25 BMP-1 TCTP MMP-12 0.857 26 RGM-C CSK MMP-12
0.857 27 CSK b-ECGF MMP-12 0.856 28 RAC1 Kallikrein 7 MMP-12 0.856
29 CDK5/p35 CSK MMP-12 0.856 30 HMG-1 MMP-7 MMP-12 0.856 31 CK-MB
GAPDH, liver MMP-12 0.855 32 RAC1 CDK5/p35 MMP-12 0.855 33 CSK
Thrombospondin-1 MMP-12 0.854 34 C9 BTK MMP-12 0.854 35 RAC1 LRIG3
MMP-12 0.854 36 HSP 90a C9 MMP-12 0.854 37 Activin A GAPDH, liver
MMP-12 0.854 38 HSP 90a BMP-1 MMP-12 0.854 39 Endostatin CSK MMP-12
0.853 40 CSK GAPDH, liver MMP-12 0.853 41 BMP-1 Cyclophilin A
MMP-12 0.853 42 ERBB1 TCTP MMP-12 0.853 43 GAPDH, liver TCTP MMP-12
0.853 44 LGMN GAPDH, liver MMP-12 0.853 45 HSP 90a LRIG3 MMP-12
0.853 46 C9 Kallikrein 7 MMP-12 0.852 47 SCF sR LRIG3 MMP-12 0.852
48 Calpain I C9 MMP-12 0.852 49 C9 Catalase MMP-12 0.852 50 HMG-1
C9 MMP-12 0.852 51 C9 LRIG3 MMP-12 0.852 52 LRIG3 Kallikrein 7
MMP-12 0.852 53 SCF sR TCTP MMP-12 0.851 54 SCF sR Cyclophilin A
MMP-12 0.851 55 BMP-1 UBE2N MMP-12 0.851 56 Kallikrein 7 TCTP
MMP-12 0.851 57 MMP-7 UBE2N MMP-12 0.851 58 MMP-7 RAC1 MMP-12 0.851
59 Kallikrein 7 Cyclophilin A MMP-12 0.850 60 MIP-5 GAPDH, liver
MMP-12 0.850 61 CSK CK-MB MMP-12 0.850 62 MMP-7 Cyclophilin A
MMP-12 0.850 63 CSK LGMN MMP-12 0.850 64 RGM-C GAPDH, liver MMP-12
0.850 65 CDK5/p35 TCTP MMP-12 0.850 66 PTN GAPDH, liver MMP-12
0.850 67 Adiponectin GAPDH, liver MMP-12 0.850 68 LRIG3 UBE2N
MMP-12 0.849 69 Thrombospondin-1 GAPDH, liver MMP-12 0.849 70 SCF
sR C9 MMP-12 0.849 71 CSK Catalase MMP-12 0.849 72 Endostatin
GAPDH, liver MMP-12 0.849 73 SCF sR RAC1 MMP-12 0.849 74 RAC1
b-ECGF MMP-12 0.849 75 TPSB2 GAPDH, liver MMP-12 0.849 76 C9 UBE2N
MMP-12 0.849 77 b-ECGF TCTP MMP-12 0.849 78 C9 IMB1 MMP-12 0.849 79
Calpain I GAPDH, liver MMP-12 0.848 80 CSK IL-8 MMP-12 0.848 81 CSK
Adiponectin MMP-12 0.848 82 Kallikrein 7 IMB1 MMP-12 0.848 83
Calpain I CSK MMP-12 0.848 84 Macrophage mannose GAPDH, liver
MMP-12 0.848 receptor 85 SCF sR CDK5/p35 MMP-12 0.848 86 IGFBP-2
Cyclophilin A MMP-12 0.848 87 CDK5/p35 BTK MMP-12 0.848 88
Macrophage mannose CSK MMP-12 0.847 receptor 89 Cadherin E IGFBP-2
MMP-12 0.847 90 Thrombospondin-1 TCTP MMP-12 0.847 91 ERBB1 C9
MMP-12 0.847 92 RAC1 RGM-C MMP-12 0.847 93 ERBB1 Cyclophilin A
MMP-12 0.847 94 CXCL16, soluble GAPDH, liver MMP-12 0.847 95 RGM-C
Cyclophilin A MMP-12 0.847 96 LRIG3 Cyclophilin A MMP-12 0.847 97
ERBB1 RAC1 MMP-12 0.847 98 Kallikrein 7 UBE2N MMP-12 0.847 99 MMP-7
LRIG3 MMP-12 0.847 100 BMP-1 BTK MMP-12 0.847
TABLE-US-00024 TABLE 24 100 Panels of four biomarkers including
MMP-12 Markers AUC 1 MMP-7 C9 GAPDH, liver MMP-12 0.892 2 C9 LRIG3
GAPDH, liver MMP-12 0.890 3 MMP-7 LRIG3 GAPDH, liver MMP-12 0.889 4
SCF sR C9 GAPDH, liver MMP-12 0.889 5 IGFBP-2 C9 GAPDH, liver
MMP-12 0.889 6 C9 LGMN GAPDH, liver MMP-12 0.889 7 IGFBP-2 MMP-7
GAPDH, liver MMP-12 0.889 8 MMP-7 BMP-1 GAPDH, liver MMP-12 0.889 9
C9 Kallikrein 7 GAPDH, liver MMP-12 0.889 10 C9 BMP-1 GAPDH, liver
MMP-12 0.888 11 ERBB1 C9 GAPDH, liver MMP-12 0.887 12 MMP-7
CDK5/p35 GAPDH, liver MMP-12 0.886 13 C9 b-ECGF GAPDH, liver MMP-12
0.886 14 Cadherin E MMP-7 GAPDH, liver MMP-12 0.885 15 MMP-7 TPSB2
GAPDH, liver MMP-12 0.885 16 Macrophage mannose C9 GAPDH, liver
MMP-12 0.885 receptor 17 MMP-7 IGFBP-7 GAPDH, liver MMP-12 0.885 18
MMP-7 CSK GAPDH, liver MMP-12 0.884 19 MMP-7 b-ECGF GAPDH, liver
MMP-12 0.884 20 C9 Adiponectin GAPDH, liver MMP-12 0.884 21 C9
TPSB2 GAPDH, liver MMP-12 0.884 22 HMG-1 MMP-7 CSK MMP-12 0.884 23
SCF sR MMP-7 GAPDH, liver MMP-12 0.884 24 MMP-7 Thrombospondin-1
GAPDH, liver MMP-12 0.883 25 CXCL16, soluble C9 GAPDH, liver MMP-12
0.883 26 MMP-7 Kallikrein 7 GAPDH, liver MMP-12 0.883 27 C9 MIP-5
GAPDH, liver MMP-12 0.883 28 SCF sR BMP-1 GAPDH, liver MMP-12 0.883
29 C9 CSK LGMN MMP-12 0.883 30 C9 GAPDH, liver LDH-H 1 MMP-12 0.882
31 C9 RGM-C GAPDH, liver MMP-12 0.882 32 Macrophage mannose MMP-7
GAPDH, liver MMP-12 0.882 receptor 33 Endostatin C9 GAPDH, liver
MMP-12 0.882 34 MMP-7 RGM-C GAPDH, liver MMP-12 0.882 35 LRIG3
Kallikrein 7 GAPDH, liver MMP-12 0.882 36 C9 Cadherin-6 GAPDH,
liver MMP-12 0.882 37 MMP-7 LRIG3 CSK MMP-12 0.882 38 MMP-7 GAPDH,
liver TCTP MMP-12 0.882 39 C9 CSK Kallikrein 7 MMP-12 0.881 40
MMP-7 GAPDH, liver LDH-H 1 MMP-12 0.881 41 MMP-7 LGMN GAPDH, liver
MMP-12 0.881 42 ERBB1 MMP-7 GAPDH, liver MMP-12 0.881 43 HMG-1
MMP-7 GAPDH, liver MMP-12 0.881 44 IGFBP-2 Kallikrein 7 GAPDH,
liver MMP-12 0.881 45 C9 Thrombospondin-1 GAPDH, liver MMP-12 0.881
46 C9 CDK5/p35 GAPDH, liver MMP-12 0.881 47 ERBB1 C9 CSK MMP-12
0.881 48 MMP-7 BMP-1 CSK MMP-12 0.881 49 Cadherin E MMP-7 CSK
MMP-12 0.881 50 SCF sR CDK5/p35 GAPDH, liver MMP-12 0.881 51 C9
RGM-C CSK MMP-12 0.881 52 C9 GAPDH, liver NACA MMP-12 0.880 53 C9
LRIG3 CSK MMP-12 0.880 54 MMP-7 Adiponectin GAPDH, liver MMP-12
0.880 55 C9 CSK GAPDH, liver MMP-12 0.880 56 LRIG3 BMP-1 GAPDH,
liver MMP-12 0.880 57 MMP-7 PTN GAPDH, liver MMP-12 0.880 58 C9 CSK
Thrombospondin-1 MMP-12 0.880 59 Activin A C9 GAPDH, liver MMP-12
0.880 60 Endostatin C9 CSK MMP-12 0.880 61 IGFBP-2 BMP-1 GAPDH,
liver MMP-12 0.880 62 IGFBP-2 LRIG3 GAPDH, liver MMP-12 0.880 63
SCF sR MMP-7 CSK MMP-12 0.880 64 Cadherin E C9 GAPDH, liver MMP-12
0.880 65 SCF sR LRIG3 GAPDH, liver MMP-12 0.880 66 Calpain I C9
GAPDH, liver MMP-12 0.879 67 RAC1 C9 Kallikrein 7 MMP-12 0.879 68
C9 LRIG3 TCTP MMP-12 0.879 69 C9 CDK5/p35 CSK MMP-12 0.879 70 C9
CSK LDH-H 1 MMP-12 0.879 71 ERBB1 MMP-7 CSK MMP-12 0.879 72 Activin
A MMP-7 GAPDH, liver MMP-12 0.879 73 IGFBP-2 Thrombospondin-1
GAPDH, liver MMP-12 0.879 74 C9 Proteinase-3 GAPDH, liver MMP-12
0.878 75 vWF C9 GAPDH, liver MMP-12 0.878 76 MMP-7 CNDP1 GAPDH,
liver MMP-12 0.878 77 C9 BMP-1 CSK MMP-12 0.878 78 C9 CK-MB GAPDH,
liver MMP-12 0.878 79 IGFBP-2 MMP-7 CSK MMP-12 0.878 80 MMP-7
GAPDH, liver Fibronectin MMP-12 0.878 81 MMP-7 CD30 Ligand GAPDH,
liver MMP-12 0.878 82 C9 CDK5/p35 TCTP MMP-12 0.878 83 C9 CNDP1
GAPDH, liver MMP-12 0.878 84 Calpain I C9 CSK MMP-12 0.878 85 MMP-7
C9 CSK MMP-12 0.877 86 MMP-7 CK-MB GAPDH, liver MMP-12 0.877 87
Calpain I MMP-7 GAPDH, liver MMP-12 0.877 88 SCF sR C9 CSK MMP-12
0.877 89 MMP-7 Cadherin-6 GAPDH, liver MMP-12 0.877 90 MMP-7
Catalase GAPDH, liver MMP-12 0.877 91 MMP-7 CDK5/p35 CSK MMP-12
0.877 92 MMP-7 RAC1 GAPDH, liver MMP-12 0.877 93 SCF sR Kallikrein
7 GAPDH, liver MMP-12 0.877 94 C9 Catalase GAPDH, liver MMP-12
0.877 95 C9 FGF-17 GAPDH, liver MMP-12 0.877 96 HMG-1 MMP-7 TCTP
MMP-12 0.877 97 ERBB1 C9 TCTP MMP-12 0.877 98 MMP-7 GAPDH, liver
NACA MMP-12 0.877 99 ERBB1 BMP-1 GAPDH, liver MMP-12 0.877 100 HSP
90a C9 LRIG3 MMP-12 0.877
TABLE-US-00025 TABLE 25 100 Panels of five biomarkers including
MMP-12 Markers AUC 1 C9 LRIG3 Kallikrein 7 GAPDH, liver MMP-12
0.900 2 MMP-7 C9 LRIG3 GAPDH, liver MMP-12 0.900 3 SCF sR C9 LRIG3
GAPDH, liver MMP-12 0.900 4 SCF sR C9 BMP-1 GAPDH, liver MMP-12
0.898 5 IGFBP-2 MMP-7 LRIG3 GAPDH, liver MMP-12 0.898 6 IGFBP-2 C9
Kallikrein 7 GAPDH, liver MMP-12 0.897 7 MMP-7 C9 BMP-1 GAPDH,
liver MMP-12 0.897 8 MMP-7 C9 TPSB2 GAPDH, liver MMP-12 0.897 9
IGFBP-2 MMP-7 Thrombospondin-1 GAPDH, liver MMP-12 0.897 10 MMP-7
C9 RGM-C GAPDH, liver MMP-12 0.897 11 HMG-1 MMP-7 C9 GAPDH, liver
MMP-12 0.897 12 Macrophage mannose C9 LRIG3 GAPDH, liver MMP-12
0.897 receptor 13 C9 LRIG3 LGMN GAPDH, liver MMP-12 0.897 14 C9
Kallikrein 7 LGMN GAPDH, liver MMP-12 0.897 15 Cadherin E MMP-7
BMP-1 GAPDH, liver MMP-12 0.897 16 Macrophage mannose C9 Kallikrein
7 GAPDH, liver MMP-12 0.897 receptor 17 MMP-7 C9 Kallikrein 7
GAPDH, liver MMP-12 0.896 18 Cadherin E IGFBP-2 MMP-7 GAPDH, liver
MMP-12 0.896 19 MMP-7 C9 b-ECGF GAPDH, liver MMP-12 0.896 20
IGFBP-2 C9 Thrombospondin-1 GAPDH, liver MMP-12 0.896 21 SCF sR C9
CDK5/p35 GAPDH, liver MMP-12 0.896 22 C9 BMP-1 LGMN GAPDH, liver
MMP-12 0.896 23 MMP-7 LRIG3 BMP-1 GAPDH, liver MMP-12 0.896 24
MMP-7 C9 CDK5/p35 GAPDH, liver MMP-12 0.896 25 SCF sR MMP-7
CDK5/p35 GAPDH, liver MMP-12 0.896 26 SCF sR MMP-7 BMP-1 GAPDH,
liver MMP-12 0.896 27 IGFBP-2 C9 LRIG3 GAPDH, liver MMP-12 0.896 28
SCF sR MMP-7 C9 GAPDH, liver MMP-12 0.895 29 Macrophage mannose
ERBB1 C9 GAPDH, liver MMP-12 0.895 receptor 30 IGFBP-2 MMP-7
Kallikrein 7 GAPDH, liver MMP-12 0.895 31 IGFBP-2 MMP-7 CDK5/p35
GAPDH, liver MMP-12 0.895 32 MMP-7 C9 GAPDH, liver LDH-H 1 MMP-12
0.895 33 IGFBP-2 MMP-7 C9 GAPDH, liver MMP-12 0.895 34 Macrophage
mannose MMP-7 C9 GAPDH, liver MMP-12 0.895 receptor 35 MMP-7
IGFBP-7 BMP-1 GAPDH, liver MMP-12 0.895 36 C9 BMP-1 Kallikrein 7
GAPDH, liver MMP-12 0.895 37 SCF sR MMP-7 LRIG3 GAPDH, liver MMP-12
0.895 38 SCF sR IGFBP-2 MMP-7 GAPDH, liver MMP-12 0.895 39 C9
b-ECGF LGMN GAPDH, liver MMP-12 0.895 40 MMP-7 C9 LGMN GAPDH, liver
MMP-12 0.895 41 SCF sR HMG-1 MMP-7 CSK MMP-12 0.895 42 IGFBP-2
MMP-7 IGFBP-7 GAPDH, liver MMP-12 0.895 43 Cadherin E MMP-7
CDK5/p35 GAPDH, liver MMP-12 0.895 44 MMP-7 C9 Thrombospondin-1
GAPDH, liver MMP-12 0.895 45 Macrophage mannose MMP-7 LRIG3 GAPDH,
liver MMP-12 0.895 receptor 46 MMP-7 IGFBP-7 LRIG3 GAPDH, liver
MMP-12 0.895 47 IGFBP-2 MMP-7 BMP-1 GAPDH, liver MMP-12 0.895 48
MMP-7 BMP-1 CSK GAPDH, liver MMP-12 0.895 49 MMP-7 LRIG3 Kallikrein
7 GAPDH, liver MMP-12 0.895 50 IGFBP-2 MMP-7 TPSB2 GAPDH, liver
MMP-12 0.895 51 MMP-7 BMP-1 GAPDH, liver LDH-H 1 MMP-12 0.894 52 C9
Kallikrein 7 TPSB2 GAPDH, liver MMP-12 0.894 53 Cadherin E MMP-7
b-ECGF GAPDH, liver MMP-12 0.894 54 SCF sR C9 Kallikrein 7 GAPDH,
liver MMP-12 0.894 55 IGFBP-2 MMP-7 GAPDH, liver LDH-H 1 MMP-12
0.894 56 SCF sR C9 Thrombospondin-1 GAPDH, liver MMP-12 0.894 57
MMP-7 BMP-1 Thrombospondin-1 GAPDH, liver MMP-12 0.894 58 MMP-7 C9
CSK GAPDH, liver MMP-12 0.894 59 Endostatin C9 LRIG3 GAPDH, liver
MMP-12 0.894 60 C9 LRIG3 Thrombospondin-1 GAPDH, liver MMP-12 0.894
61 C9 LRIG3 BMP-1 GAPDH, liver MMP-12 0.894 62 IGFBP-2 MMP-7 b-ECGF
GAPDH, liver MMP-12 0.894 63 IGFBP-2 MMP-7 LGMN GAPDH, liver MMP-12
0.894 64 MMP-7 C9 Cadherin-6 GAPDH, liver MMP-12 0.894 65 Cadherin
E MMP-7 C9 GAPDH, liver MMP-12 0.894 66 HMG-1 MMP-7 C9 CSK MMP-12
0.894 67 SCF sR C9 Adiponectin GAPDH, liver MMP-12 0.894 68 CXCL16,
soluble C9 LRIG3 GAPDH, liver MMP-12 0.894 69 IGFBP-2 MMP-7 CSK
GAPDH, liver MMP-12 0.894 70 SCF sR Macrophage C9 GAPDH, liver
MMP-12 0.894 mannose receptor 71 MMP-7 C9 Adiponectin GAPDH, liver
MMP-12 0.894 72 Cadherin E MMP-7 TPSB2 GAPDH, liver MMP-12 0.894 73
C9 LRIG3 Cadherin-6 GAPDH, liver MMP-12 0.894 74 MMP-7 LRIG3 CSK
GAPDH, liver MMP-12 0.894 75 MMP-7 LRIG3 Thrombospondin-1 GAPDH,
liver MMP-12 0.894 76 ERBB1 MMP-7 C9 GAPDH, liver MMP-12 0.894 77
ERBB1 C9 BMP-1 GAPDH, liver MMP-12 0.894 78 ERBB1 C9 LGMN GAPDH,
liver MMP-12 0.894 79 C9 Adiponectin LGMN GAPDH, liver MMP-12 0.894
80 MMP-7 BMP-1 CDK5/p35 GAPDH, liver MMP-12 0.893 81 MMP-7 BMP-1
LGMN GAPDH, liver MMP-12 0.893 82 C9 LRIG3 GAPDH, liver NACA MMP-12
0.893 83 C9 Kallikrein 7 GAPDH, liver LDH-H 1 MMP-12 0.893 84 C9
Kallikrein 7 Adiponectin GAPDH, liver MMP-12 0.893 85 IGFBP-2 MMP-7
Cadherin-6 GAPDH, liver MMP-12 0.893 86 IGFBP-2 C9 LGMN GAPDH,
liver MMP-12 0.893 87 MMP-7 BMP-1 TPSB2 GAPDH, liver MMP-12 0.893
88 C9 LRIG3 Adiponectin GAPDH, liver MMP-12 0.893 89 C9 TPSB2 LGMN
GAPDH, liver MMP-12 0.893 90 MMP-7 C9 CD30 Ligand GAPDH, liver
MMP-12 0.893 91 Cadherin E MMP-7 LRIG3 GAPDH, liver MMP-12 0.893 92
SCF sR IGFBP-2 C9 GAPDH, liver MMP-12 0.893 93 HMG-1 IGFBP-2 MMP-7
GAPDH, liver MMP-12 0.893 94 SCF sR Cadherin E MMP-7 GAPDH, liver
MMP-12 0.893 95 SCF sR MMP-7 Thrombospondin-1 GAPDH, liver MMP-12
0.893 96 IGFBP-2 C9 BMP-1 GAPDH, liver MMP-12 0.893 97 MMP-7 LRIG3
GAPDH, liver TCTP MMP-12 0.893 98 C9 LRIG3 GAPDH, liver LDH-H 1
MMP-12 0.893 99 SCF sR C9 TPSB2 GAPDH, liver MMP-12 0.893 100
IGFBP-2 Macrophage MMP-7 GAPDH, liver MMP-12 0.893 mannose
receptor
* * * * *
References